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Methodology, Parameters, and Calculations

Keywords

health economics methodology, clinical trial cost analysis, medical research ROI, cost-benefit analysis healthcare, sensitivity analysis, Monte Carlo simulation, DALY calculation, pragmatic clinical trials

Overview

This appendix documents all 218 parameters used in the analysis, organized by type:

External sources (peer-reviewed): 89
Calculated values: 99
Core definitions: 30

Calculated Values

Parameters derived from mathematical formulas and economic models.

Combination Therapy Space: 45.1B combinations

📊 Details

Total combination therapy space (pairwise drug combinations × diseases). Standard in oncology, HIV, cardiology.

Inputs:

Pairwise Drug Combinations 🔢: 45.1M combinations
Trial-Relevant Diseases: 1.00k diseases (95% CI: 800 diseases - 1.20k diseases)

\[ \begin{gathered} Space_{combo} \\ = N_{combo} \times N_{diseases,trial} \\ = 45.1M \times 1{,}000 \\ = 45.1B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Pairwise Drug Combinations: 45.1M combinations

📊 Details

Unique pairwise drug combinations from known safe compounds (n choose 2)

Inputs:

Safe Compounds Available for Testing: 9.50k compounds (95% CI: 7.00k compounds - 12.0k compounds)

Formula: SAFE_COMPOUNDS × (SAFE_COMPOUNDS - 1) ÷ 2

✓ High confidence

Sensitivity Analysis

Combination Therapy Exploration Time (Current): 13.7M years

📊 Details

Years to test all pairwise drug combinations at current trial capacity. Combination therapy is standard in oncology, HIV, cardiology.

Inputs:

Combination Therapy Space 🔢: 45.1B combinations
Current Global Clinical Trials per Year 📊: 3.30k trials/year (95% CI: 2.64k trials/year - 3.96k trials/year)

\[ \begin{gathered} T_{explore,combo} \\ = \frac{Space_{combo}}{Trials_{ann,curr}} \\ = \frac{45.1B}{3{,}300} \\ = 13.7M \\[0.5em] \text{where } Space_{combo} \\ = N_{combo} \times N_{diseases,trial} \\ = 45.1M \times 1{,}000 \\ = 45.1B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Combination Therapy Exploration Time (Current)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Current Trials Per Year	-0.9931	Strong driver

Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.

Monte Carlo Distribution

Simulation Results Summary: Combination Therapy Exploration Time (Current)

Statistic	Value
Baseline (deterministic)	13.7M
Mean (expected value)	13.8M
Median (50th percentile)	13.8M
Standard Deviation	1.36M
90% Confidence Interval	[11.6M, 16.3M]

The histogram shows the distribution of Combination Therapy Exploration Time (Current) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Combination Therapy Exploration Time (Current)

This exceedance probability chart shows the likelihood that Combination Therapy Exploration Time (Current) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Known Safe Exploration Time (Current): 2.88k years

📊 Details

Years to test all known safe drug-disease combinations at current global trial capacity

Inputs:

Possible Drug-Disease Combinations 🔢: 9.50M combinations
Current Global Clinical Trials per Year 📊: 3.30k trials/year (95% CI: 2.64k trials/year - 3.96k trials/year)

\[ \begin{gathered} T_{explore,safe} \\ = \frac{N_{combos}}{Trials_{ann,curr}} \\ = \frac{9.5M}{3{,}300} \\ = 2{,}880 \\[0.5em] \text{where } N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Known Safe Exploration Time (Current)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Current Trials Per Year	-0.9931	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Known Safe Exploration Time (Current)

Statistic	Value
Baseline (deterministic)	2.88k
Mean (expected value)	2.91k
Median (50th percentile)	2.90k
Standard Deviation	286
90% Confidence Interval	[2.45k, 3.43k]

The histogram shows the distribution of Known Safe Exploration Time (Current) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Known Safe Exploration Time (Current)

This exceedance probability chart shows the likelihood that Known Safe Exploration Time (Current) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Current Patient Participation Rate in Clinical Trials: 0.0792%

📊 Details

Current patient participation rate in clinical trials (0.08% = 1.9M participants / 2.4B disease patients)

Inputs:

Annual Global Clinical Trial Participants 📊: 1.90M patients/year (95% CI: 1.50M patients/year - 2.30M patients/year)
Global Population with Chronic Diseases 📊: 2.40B people (95% CI: 2.00B people - 2.80B people)

\[ \begin{gathered} Rate_{part} \\ = \frac{Slots_{curr}}{N_{patients}} \\ = \frac{1.9M}{2.4B} \\ = 0.0792\% \end{gathered} \]

Methodology:²⁵

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Current Patient Participation Rate in Clinical Trials

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Current Disease Patients Global	4.1698	Strong driver
Current Trial Slots Available	-3.1720	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Current Patient Participation Rate in Clinical Trials

Statistic	Value
Baseline (deterministic)	0.0792%
Mean (expected value)	0.079%
Median (50th percentile)	0.079%
Standard Deviation	0.00169%
90% Confidence Interval	[0.0761%, 0.0819%]

The histogram shows the distribution of Current Patient Participation Rate in Clinical Trials across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Current Patient Participation Rate in Clinical Trials

This exceedance probability chart shows the likelihood that Current Patient Participation Rate in Clinical Trials will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Decentralized Framework for Drug Assessment Operational Costs: $40M

📊 Details

Total annual Decentralized Framework for Drug Assessment operational costs (sum of all components: $15M + $10M + $8M + $5M + $2M)

Inputs:

Decentralized Framework for Drug Assessment Maintenance Costs: $15M (95% CI: $10M - $22M)
Decentralized Framework for Drug Assessment Staff Costs: $10M (95% CI: $7M - $15M)
Decentralized Framework for Drug Assessment Infrastructure Costs: $8M (95% CI: $5M - $12M)
Decentralized Framework for Drug Assessment Regulatory Coordination Costs: $5M (95% CI: $3M - $8M)
Decentralized Framework for Drug Assessment Community Support Costs: $2M (95% CI: $1M - $3M)

\[ \begin{gathered} OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Decentralized Framework for Drug Assessment Operational Costs

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA OPEX Platform Maintenance	0.3542	Moderate driver
dFDA OPEX Staff	0.2355	Weak driver
dFDA OPEX Infrastructure	0.2060	Weak driver
dFDA OPEX Regulatory	0.1469	Weak driver
dFDA OPEX Community	0.0576	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Annual Decentralized Framework for Drug Assessment Operational Costs

Statistic	Value
Baseline (deterministic)	$40M
Mean (expected value)	$39.9M
Median (50th percentile)	$39M
Standard Deviation	$8.21M
90% Confidence Interval	[$27.3M, $55.6M]

The histogram shows the distribution of Total Annual Decentralized Framework for Drug Assessment Operational Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Decentralized Framework for Drug Assessment Operational Costs

This exceedance probability chart shows the likelihood that Total Annual Decentralized Framework for Drug Assessment Operational Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings: $58.6B

📊 Details

Annual Decentralized Framework for Drug Assessment benefit from R&D savings (trial cost reduction, secondary component)

Inputs:

Annual Global Spending on Clinical Trials 📊: $60B (95% CI: $50B - $75B)
dFDA Trial Cost Reduction Percentage 🔢: 97.7%

\[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Clinical Trials Spending Annual	1.0205	Strong driver
dFDA Trial Cost Reduction %	0.0244	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings

Statistic	Value
Baseline (deterministic)	$58.6B
Mean (expected value)	$58.8B
Median (50th percentile)	$57.8B
Standard Deviation	$7.66B
90% Confidence Interval	[$49.2B, $73.1B]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Direct Funding Cost per DALY: $0.841

📊 Details

Cost per DALY if philanthropists/governments directly funded $21.76B/year for ~46.5 years (therapeutic space exploration period, NPV: ~$541.9B) instead of treaty campaign ($1B). Treaty achieves 542× leverage: $1B campaign unlocks government funding for 46.5 years (NPV: $541.9B), avoiding direct philanthropic commitment. Both achieve same 200B DALY timeline shift benefit. Still cost-effective vs bed nets ($89.0/DALY).

Inputs:

dFDA Direct Funding NPV (Exploration Period) 🔢: $475B
Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput 🔢: 565B DALYs

\[ \begin{gathered} Cost_{direct,DALY} = \frac{NPV_{direct}}{DALYs_{max}} = \frac{\$475B}{565B} = \$0.841 \\[0.5em] \text{where } NPV_{direct} \\ = \frac{T_{queue,dFDA}}{Treasury_{RD,ann} \times r_{discount}} \\ = \frac{36}{\$21.8B \times 3\%} \\ = \$475B \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } T_{queue,dFDA} = \frac{T_{queue,SQ}}{k_{capacity}} = \frac{443}{12.3} = 36 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Direct Funding Cost per DALY

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Plus Efficacy Lag DALYs	-0.5182	Strong driver
dFDA Direct Funding Queue Clearance NPV	0.4583	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: dFDA Direct Funding Cost per DALY

Statistic	Value
Baseline (deterministic)	$0.841
Mean (expected value)	$0.800
Median (50th percentile)	$0.695
Standard Deviation	$0.466
90% Confidence Interval	[$0.242, $1.75]

The histogram shows the distribution of dFDA Direct Funding Cost per DALY across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Direct Funding Cost per DALY

This exceedance probability chart shows the likelihood that dFDA Direct Funding Cost per DALY will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Direct Funding NPV (Exploration Period): $475B

📊 Details

NPV of direct funding ($21.76B/year for medical research after bond/IAB allocations) for the ~46.5-year therapeutic space exploration period. Alternative scenario: instead of $1B treaty campaign to unlock government funding, philanthropists/NIH directly fund clinical trials until the therapeutic space is fully explored. Funding period is exploration time (46.5 years with 9.5× trial capacity), not timeline shift amount (207 years). After exploration completes, the timeline shift benefit (200B DALYs) is fully realized.

Inputs:

Annual Funding for Pragmatic Clinical Trials 🔢: $21.8B
Standard Discount Rate for NPV Analysis: 3%
dFDA Therapeutic Space Exploration Time 🔢: 36 years

\[ NPV_{direct} = Funding_{ann} \times \frac{1 - (1+r)^{-T}}{r} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Direct Funding NPV (Exploration Period)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Queue Clearance Years	0.9443	Strong driver

Monte Carlo Distribution

Simulation Results Summary: dFDA Direct Funding NPV (Exploration Period)

Statistic	Value
Baseline (deterministic)	$475B
Mean (expected value)	$425B
Median (50th percentile)	$424B
Standard Deviation	$135B
90% Confidence Interval	[$211B, $651B]

The histogram shows the distribution of dFDA Direct Funding NPV (Exploration Period) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Direct Funding NPV (Exploration Period)

This exceedance probability chart shows the likelihood that dFDA Direct Funding NPV (Exploration Period) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total DALYs Lost from Disease Eradication Delay: 7.94B DALYs

📊 Details

Total Disability-Adjusted Life Years lost from disease eradication delay (PRIMARY estimate)

Inputs:

Years of Life Lost from Disease Eradication Delay 🔢: 7.07B years
Years Lived with Disability During Disease Eradication Delay 🔢: 873M years

\[ \begin{gathered} DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.07B + 873M = 7.94B \\[0.5em] \text{where } YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Total DALYs Lost from Disease Eradication Delay

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Efficacy Lag Elimination Yll	0.7043	Strong driver
dFDA Efficacy Lag Elimination Yld	0.3107	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Total DALYs Lost from Disease Eradication Delay

Statistic	Value
Baseline (deterministic)	7.94B
Mean (expected value)	8.05B
Median (50th percentile)	7.89B
Standard Deviation	2.31B
90% Confidence Interval	[4.43B, 12.1B]

The histogram shows the distribution of Total DALYs Lost from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total DALYs Lost from Disease Eradication Delay

This exceedance probability chart shows the likelihood that Total DALYs Lost from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Deaths from Disease Eradication Delay: 416M deaths

📊 Details

Total eventually avoidable deaths from delaying disease eradication by 8.2 years (PRIMARY estimate, conservative). Excludes fundamentally unavoidable deaths (primarily accidents ~7.9%).

Inputs:

Regulatory Delay for Efficacy Testing Post-Safety Verification 📊: 8.2 years (SE: ±2 years)
Global Daily Deaths from Disease and Aging 📊: 150k deaths/day (SE: ±7.50k deaths/day)

\[ \begin{gathered} Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Total Deaths from Disease Eradication Delay

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Efficacy Lag Years	1.1404	Strong driver
Global Disease Deaths Daily	-0.1422	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Total Deaths from Disease Eradication Delay

Statistic	Value
Baseline (deterministic)	416M
Mean (expected value)	420M
Median (50th percentile)	414M
Standard Deviation	122M
90% Confidence Interval	[225M, 630M]

The histogram shows the distribution of Total Deaths from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Deaths from Disease Eradication Delay

This exceedance probability chart shows the likelihood that Total Deaths from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Economic Loss from Disease Eradication Delay: $1.19 quadrillion

📊 Details

Total economic loss from delaying disease eradication by 8.2 years (PRIMARY estimate, 2024 USD). Values global DALYs at standardized US/International normative rate ($150k) rather than local ability-to-pay, representing the full human capital loss.

Inputs:

Total DALYs Lost from Disease Eradication Delay 🔢: 7.94B DALYs
Standard Economic Value per QALY 📊: $150K (SE: ±$30K)

\[ \begin{gathered} Value_{lag} \\ = DALYs_{lag} \times Value_{QALY} \\ = 7.94B \times \$150K \\ = \$1190T \\[0.5em] \text{where } DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.07B + 873M = 7.94B \\[0.5em] \text{where } YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Total Economic Loss from Disease Eradication Delay

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Efficacy Lag Elimination DALYs	1.0671	Strong driver
Standard Economic QALY Value Usd	-0.0733	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Economic Loss from Disease Eradication Delay

Statistic	Value
Baseline (deterministic)	$1.19 quadrillion
Mean (expected value)	$1.27 quadrillion
Median (50th percentile)	$1.18 quadrillion
Standard Deviation	$581T
90% Confidence Interval	[$443T, $2.41 quadrillion]

The histogram shows the distribution of Total Economic Loss from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Economic Loss from Disease Eradication Delay

This exceedance probability chart shows the likelihood that Total Economic Loss from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Years Lived with Disability During Disease Eradication Delay: 873M years

📊 Details

Years Lived with Disability during disease eradication delay (PRIMARY estimate)

Inputs:

Total Deaths from Disease Eradication Delay 🔢: 416M deaths
Pre-Death Suffering Period During Post-Safety Efficacy Delay 📊: 6 years (95% CI: 4 years - 9 years)
Disability Weight for Untreated Chronic Conditions 📊: 0.35 weight (SE: ±0.07 weight)

\[ \begin{gathered} YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Years Lived with Disability During Disease Eradication Delay

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Regulatory Delay Suffering Period Years	2.0883	Strong driver
Chronic Disease Disability Weight	-0.9003	Strong driver
dFDA Efficacy Lag Elimination Deaths Averted	-0.2255	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Years Lived with Disability During Disease Eradication Delay

Statistic	Value
Baseline (deterministic)	873M
Mean (expected value)	1.02B
Median (50th percentile)	846M
Standard Deviation	716M
90% Confidence Interval	[217M, 2.43B]

The histogram shows the distribution of Years Lived with Disability During Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Years Lived with Disability During Disease Eradication Delay

This exceedance probability chart shows the likelihood that Years Lived with Disability During Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Years of Life Lost from Disease Eradication Delay: 7.07B years

📊 Details

Years of Life Lost from disease eradication delay deaths (PRIMARY estimate)

Inputs:

Total Deaths from Disease Eradication Delay 🔢: 416M deaths
Global Life Expectancy (2024) 📊: 79 years (SE: ±2 years)
Mean Age of Preventable Death from Post-Safety Efficacy Delay 📊: 62 years (SE: ±3 years)

\[ \begin{gathered} YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Years of Life Lost from Disease Eradication Delay

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Life Expectancy 2024	2.0066	Strong driver
Regulatory Delay Mean Age Of Death	-1.3852	Strong driver
dFDA Efficacy Lag Elimination Deaths Averted	0.3779	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Years of Life Lost from Disease Eradication Delay

Statistic	Value
Baseline (deterministic)	7.07B
Mean (expected value)	7.03B
Median (50th percentile)	7.05B
Standard Deviation	1.62B
90% Confidence Interval	[4.21B, 9.68B]

The histogram shows the distribution of Years of Life Lost from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Years of Life Lost from Disease Eradication Delay

This exceedance probability chart shows the likelihood that Years of Life Lost from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA New Treatments Per Year: 185 diseases/year

📊 Details

Diseases per year receiving their first effective treatment with dFDA. Scales proportionally with trial capacity multiplier.

Inputs:

Diseases Getting First Treatment Per Year 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year)
Trial Capacity Multiplier 🔢: 12.3x

\[ \begin{gathered} Treatments_{dFDA,ann} \\ = Treatments_{new,ann} \times k_{capacity} \\ = 15 \times 12.3 \\ = 185 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for dFDA New Treatments Per Year

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Multiplier	0.9380	Strong driver
New Disease First Treatments Per Year	-0.0784	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: dFDA New Treatments Per Year

Statistic	Value
Baseline (deterministic)	185
Mean (expected value)	254
Median (50th percentile)	224
Standard Deviation	140
90% Confidence Interval	[107, 490]

The histogram shows the distribution of dFDA New Treatments Per Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA New Treatments Per Year

This exceedance probability chart shows the likelihood that dFDA New Treatments Per Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only): $58.6B

📊 Details

Annual net savings from R&D cost reduction only (gross savings minus operational costs, excludes regulatory delay value)

Inputs:

Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $58.6B
Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M

\[ \begin{gathered} Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{dFDA} \\ = \$58.6B - \$40M \\ = \$58.6B \\[0.5em] \text{where } Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Benefit R&D Only Annual	1.0011	Strong driver
dFDA Annual OPEX	-0.0011	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only)

Statistic	Value
Baseline (deterministic)	$58.6B
Mean (expected value)	$58.8B
Median (50th percentile)	$57.8B
Standard Deviation	$7.66B
90% Confidence Interval	[$49.2B, $73B]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only)

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Total NPV Annual OPEX: $40M

📊 Details

Total NPV annual opex (Decentralized Framework for Drug Assessment core + DIH initiatives)

Inputs:

Decentralized Framework for Drug Assessment Core framework Annual OPEX: $18.9M (95% CI: $11M - $26.5M)
DIH Broader Initiatives Annual OPEX: $21.1M (95% CI: $14M - $32M)

\[ \begin{gathered} OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Total NPV Annual OPEX

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
DIH NPV Annual OPEX Initiatives	0.5419	Strong driver
dFDA NPV Annual OPEX	0.4592	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Total NPV Annual OPEX

Statistic	Value
Baseline (deterministic)	$40M
Mean (expected value)	$39.9M
Median (50th percentile)	$39.1M
Standard Deviation	$8.04M
90% Confidence Interval	[$27.5M, $55.4M]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Total NPV Annual OPEX across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Total NPV Annual OPEX

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Total NPV Annual OPEX will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted): $389B

📊 Details

NPV of Decentralized Framework for Drug Assessment R&D savings only with 5-year adoption ramp (10-year horizon, most conservative financial estimate)

Inputs:

Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only) 🔢: $58.6B
Standard Discount Rate for NPV Analysis: 3%

\[ \begin{gathered} NPV_{RD} \\ = \sum_{t=1}^{10} \frac{Savings_{RD,ann} \times \frac{\min(t,5)}{5}}{(1+r)^t} \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Net Savings R&D Only Annual	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted)

Statistic	Value
Baseline (deterministic)	$389B
Mean (expected value)	$391B
Median (50th percentile)	$384B
Standard Deviation	$50.9B
90% Confidence Interval	[$327B, $485B]

The histogram shows the distribution of NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted)

This exceedance probability chart shows the likelihood that NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

NPV Net Benefit (R&D Only): $389B

📊 Details

NPV net benefit using R&D savings only (benefits minus costs)

Inputs:

NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted) 🔢: $389B
Decentralized Framework for Drug Assessment Total NPV Cost 🔢: $611M

\[ \begin{gathered} NPV_{net,RD} \\ = NPV_{RD} - Cost_{dFDA,total} \\ = \$389B - \$611M \\ = \$389B \\[0.5em] \text{where } NPV_{RD} = \sum_{t=1}^{10} \frac{Savings_{RD,ann} \times \frac{\min(t,5)}{5}}{(1+r)^t} \\[0.5em] \text{where } Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{dFDA} \\ = \$58.6B - \$40M \\ = \$58.6B \\[0.5em] \text{where } Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Cost_{dFDA,total} \\ = PV_{OPEX} + Cost_{upfront,total} \\ = \$342M + \$270M \\ = \$611M \\[0.5em] \text{where } PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \\[0.5em] \text{where } OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \\[0.5em] \text{where } Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for NPV Net Benefit (R&D Only)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA NPV Benefit R&D Only	1.0025	Strong driver
dFDA NPV Total Cost	-0.0025	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: NPV Net Benefit (R&D Only)

Statistic	Value
Baseline (deterministic)	$389B
Mean (expected value)	$390B
Median (50th percentile)	$383B
Standard Deviation	$50.7B
90% Confidence Interval	[$326B, $484B]

The histogram shows the distribution of NPV Net Benefit (R&D Only) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: NPV Net Benefit (R&D Only)

This exceedance probability chart shows the likelihood that NPV Net Benefit (R&D Only) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years: $342M

📊 Details

Present value of annual opex over 10 years (NPV formula)

Inputs:

Decentralized Framework for Drug Assessment Total NPV Annual OPEX 🔢: $40M
Standard Discount Rate for NPV Analysis: 3%
Standard Time Horizon for NPV Analysis: 10 years

\[ PV_{OPEX} = OPEX_{ann} \times \frac{1 - (1+r)^{-T}}{r} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA NPV Annual OPEX Total	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years

Statistic	Value
Baseline (deterministic)	$342M
Mean (expected value)	$340M
Median (50th percentile)	$333M
Standard Deviation	$68.6M
90% Confidence Interval	[$235M, $473M]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Total NPV Cost: $611M

📊 Details

Total NPV cost (upfront + PV of annual opex)

Inputs:

Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years 🔢: $342M
Decentralized Framework for Drug Assessment Total NPV Upfront Costs 🔢: $270M

\[ \begin{gathered} Cost_{dFDA,total} \\ = PV_{OPEX} + Cost_{upfront,total} \\ = \$342M + \$270M \\ = \$611M \\[0.5em] \text{where } PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \\[0.5em] \text{where } OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \\[0.5em] \text{where } Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Total NPV Cost

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA NPV Pv Annual OPEX	0.5417	Strong driver
dFDA NPV Upfront Cost Total	0.4585	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Total NPV Cost

Statistic	Value
Baseline (deterministic)	$611M
Mean (expected value)	$609M
Median (50th percentile)	$595M
Standard Deviation	$127M
90% Confidence Interval	[$415M, $853M]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Total NPV Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Total NPV Cost

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Total NPV Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Total NPV Upfront Costs: $270M

📊 Details

Total NPV upfront costs (Decentralized Framework for Drug Assessment core + DIH initiatives)

Inputs:

Decentralized Framework for Drug Assessment Core framework Build Cost: $40M (95% CI: $25M - $65M)
DIH Broader Initiatives Upfront Cost: $230M (95% CI: $150M - $350M)

\[ \begin{gathered} Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Total NPV Upfront Costs

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
DIH NPV Upfront Cost Initiatives	0.8338	Strong driver
dFDA NPV Upfront Cost	0.1662	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Total NPV Upfront Costs

Statistic	Value
Baseline (deterministic)	$270M
Mean (expected value)	$269M
Median (50th percentile)	$262M
Standard Deviation	$58.1M
90% Confidence Interval	[$181M, $380M]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Total NPV Upfront Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Total NPV Upfront Costs

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Total NPV Upfront Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Overhead Percentage of Treaty Funding: 0.147%

📊 Details

Percentage of treaty funding allocated to Decentralized Framework for Drug Assessment framework overhead

Inputs:

Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M
Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $27.2B

\[ \begin{gathered} OPEX_{pct} = \frac{OPEX_{dFDA}}{Funding_{treaty}} = \frac{\$40M}{\$27.2B} = 0.147\% \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Overhead Percentage of Treaty Funding

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Annual OPEX	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Overhead Percentage of Treaty Funding

Statistic	Value
Baseline (deterministic)	0.147%
Mean (expected value)	0.147%
Median (50th percentile)	0.143%
Standard Deviation	0.0302%
90% Confidence Interval	[0.1%, 0.204%]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Overhead Percentage of Treaty Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Overhead Percentage of Treaty Funding

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Overhead Percentage of Treaty Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Therapeutic Space Exploration Time: 36 years

📊 Details

Years to explore the entire therapeutic search space with dFDA implementation. At increased discovery rate, finding first treatments for all currently untreatable diseases takes ~36 years instead of ~443.

Inputs:

Status Quo Therapeutic Space Exploration Time 🔢: 443 years
Trial Capacity Multiplier 🔢: 12.3x

\[ \begin{gathered} T_{queue,dFDA} = \frac{T_{queue,SQ}}{k_{capacity}} = \frac{443}{12.3} = 36 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Therapeutic Space Exploration Time

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Status Quo Queue Clearance Years	-1.3321	Strong driver
dFDA Trial Capacity Multiplier	0.4867	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: dFDA Therapeutic Space Exploration Time

Statistic	Value
Baseline (deterministic)	36
Mean (expected value)	34.6
Median (50th percentile)	29.7
Standard Deviation	19.9
90% Confidence Interval	[11.6, 77.2]

The histogram shows the distribution of dFDA Therapeutic Space Exploration Time across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Therapeutic Space Exploration Time

This exceedance probability chart shows the likelihood that dFDA Therapeutic Space Exploration Time will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

ROI from Decentralized Framework for Drug Assessment R&D Savings Only: 637:1

📊 Details

ROI from Decentralized Framework for Drug Assessment R&D savings only (10-year NPV, most conservative estimate)

Inputs:

NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted) 🔢: $389B
Decentralized Framework for Drug Assessment Total NPV Cost 🔢: $611M

\[ \begin{gathered} ROI_{RD} = \frac{NPV_{RD}}{Cost_{dFDA,total}} = \frac{\$389B}{\$611M} = 637 \\[0.5em] \text{where } NPV_{RD} = \sum_{t=1}^{10} \frac{Savings_{RD,ann} \times \frac{\min(t,5)}{5}}{(1+r)^t} \\[0.5em] \text{where } Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{dFDA} \\ = \$58.6B - \$40M \\ = \$58.6B \\[0.5em] \text{where } Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Cost_{dFDA,total} \\ = PV_{OPEX} + Cost_{upfront,total} \\ = \$342M + \$270M \\ = \$611M \\[0.5em] \text{where } PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \\[0.5em] \text{where } OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \\[0.5em] \text{where } Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for ROI from Decentralized Framework for Drug Assessment R&D Savings Only

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA NPV Total Cost	-2.6305	Strong driver
dFDA NPV Benefit R&D Only	1.7615	Strong driver

Monte Carlo Distribution

Simulation Results Summary: ROI from Decentralized Framework for Drug Assessment R&D Savings Only

Statistic	Value
Baseline (deterministic)	637:1
Mean (expected value)	653:1
Median (50th percentile)	645:1
Standard Deviation	58.4:1
90% Confidence Interval	[569:1, 790:1]

The histogram shows the distribution of ROI from Decentralized Framework for Drug Assessment R&D Savings Only across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: ROI from Decentralized Framework for Drug Assessment R&D Savings Only

This exceedance probability chart shows the likelihood that ROI from Decentralized Framework for Drug Assessment R&D Savings Only will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Maximum Trials per Year: 40.6k trials/year

📊 Details

Maximum trials per year possible with trial capacity multiplier

Inputs:

Current Global Clinical Trials per Year 📊: 3.30k trials/year (95% CI: 2.64k trials/year - 3.96k trials/year)
Trial Capacity Multiplier 🔢: 12.3x

\[ \begin{gathered} Capacity_{trials} \\ = Trials_{ann,curr} \times k_{capacity} \\ = 3{,}300 \times 12.3 \\ = 40{,}600 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Maximum Trials per Year

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Multiplier	0.9321	Strong driver
Current Trials Per Year	-0.0802	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Maximum Trials per Year

Statistic	Value
Baseline (deterministic)	40.6k
Mean (expected value)	67.3k
Median (50th percentile)	52.4k
Standard Deviation	53.1k
90% Confidence Interval	[16.3k, 170k]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Maximum Trials per Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Maximum Trials per Year

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Maximum Trials per Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Trial Capacity Multiplier: 12.3x

📊 Details

Trial capacity multiplier from DIH funding capacity vs. current global trial participation

Inputs:

Annual Global Clinical Trial Participants 📊: 1.90M patients/year (95% CI: 1.50M patients/year - 2.30M patients/year)
Patients Fundable Annually 🔢: 23.4M patients/year

\[ \begin{gathered} k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Trial Capacity Multiplier

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
DIH Patients Fundable Annually	1.0768	Strong driver
Current Trial Slots Available	0.0910	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Trial Capacity Multiplier

Statistic	Value
Baseline (deterministic)	12.3x
Mean (expected value)	22.1x
Median (50th percentile)	16x
Standard Deviation	20.2x
90% Confidence Interval	[4.19x, 61.3x]

The histogram shows the distribution of Trial Capacity Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Trial Capacity Multiplier

This exceedance probability chart shows the likelihood that Trial Capacity Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 565B DALYs

📊 Details

Total DALYs averted from the combined dFDA timeline shift. Calculated as annual global DALY burden × eventually avoidable percentage × timeline shift years. Includes both fatal and non-fatal diseases (WHO GBD methodology).

Inputs:

Global Annual DALY Burden 📊: 2.88B DALYs/year (SE: ±150M DALYs/year)
Eventually Avoidable DALY Percentage: 92.6% (95% CI: 50% - 98%)
dFDA Average Total Timeline Shift 🔢: 212 years

\[ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Plus Efficacy Lag Years	0.9001	Strong driver
Eventually Avoidable DALY %	0.4864	Moderate driver
Global Annual DALY Burden	0.0433	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Statistic	Value
Baseline (deterministic)	565B
Mean (expected value)	610B
Median (50th percentile)	614B
Standard Deviation	148B
90% Confidence Interval	[361B, 877B]

The histogram shows the distribution of Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

This exceedance probability chart shows the likelihood that Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: $84.8 quadrillion

📊 Details

Total economic value from the combined dFDA timeline shift. DALYs valued at standard economic rate.

Inputs:

Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput 🔢: 565B DALYs
Standard Economic Value per QALY 📊: $150K (SE: ±$30K)

\[ \begin{gathered} Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Plus Efficacy Lag DALYs	1.7790	Strong driver
Standard Economic QALY Value Usd	1.3377	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Statistic	Value
Baseline (deterministic)	$84.8 quadrillion
Mean (expected value)	$87.8 quadrillion
Median (50th percentile)	$92.8 quadrillion
Standard Deviation	$11.5 quadrillion
90% Confidence Interval	[$62.4 quadrillion, $97.3 quadrillion]

The histogram shows the distribution of Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

This exceedance probability chart shows the likelihood that Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 10.7B deaths

📊 Details

Total eventually avoidable deaths from the combined dFDA timeline shift. Represents deaths prevented when cures arrive earlier due to both increased trial capacity and eliminated efficacy lag.

Inputs:

Global Daily Deaths from Disease and Aging 📊: 150k deaths/day (SE: ±7.50k deaths/day)
dFDA Average Total Timeline Shift 🔢: 212 years

\[ \begin{gathered} Lives_{max} \\ = Deaths_{disease,daily} \times T_{accel,max} \times 338 \\ = 150{,}000 \times 212 \times 338 \\ = 10.7B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Plus Efficacy Lag Years	1.0375	Strong driver
Global Disease Deaths Daily	0.0407	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Statistic	Value
Baseline (deterministic)	10.7B
Mean (expected value)	11.7B
Median (50th percentile)	11.7B
Standard Deviation	2.45B
90% Confidence Interval	[7.39B, 16.2B]

The histogram shows the distribution of Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

This exceedance probability chart shows the likelihood that Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 1931T hours

📊 Details

Hours of suffering eliminated from the combined dFDA timeline shift. Calculated from YLD component of DALYs (39% of total DALYs × hours per year). One-time benefit, not annual recurring.

Inputs:

Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput 🔢: 565B DALYs
YLD Proportion of Total DALYs 📊: 0.39 proportion (SE: ±0.03 proportion)

\[ \begin{gathered} Hours_{suffer,max} \\ = DALYs_{max} \times Pct_{YLD} \times 8760 \\ = 565B \times 0.39 \times 8760 \\ = 1930T \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Plus Efficacy Lag DALYs	1.3101	Strong driver
Global Yld Proportion Of DALYs	0.3975	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Statistic	Value
Baseline (deterministic)	1931T
Mean (expected value)	2049T
Median (50th percentile)	2107T
Standard Deviation	374T
90% Confidence Interval	[1362T, 2616T]

The histogram shows the distribution of Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

This exceedance probability chart shows the likelihood that Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Average Total Timeline Shift: 212 years

📊 Details

Average years earlier patients receive treatments due to dFDA. Combines treatment timeline acceleration from increased trial capacity with efficacy lag elimination for treatments already discovered.

Inputs:

dFDA Treatment Timeline Acceleration 🔢: 204 years
Regulatory Delay for Efficacy Testing Post-Safety Verification 📊: 8.2 years (SE: ±2 years)

\[ \begin{gathered} T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Average Total Timeline Shift

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Treatment Acceleration Years	1.0325	Strong driver
Efficacy Lag Years	0.0327	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: dFDA Average Total Timeline Shift

Statistic	Value
Baseline (deterministic)	212
Mean (expected value)	233
Median (50th percentile)	231
Standard Deviation	60.3
90% Confidence Interval	[135, 355]

The histogram shows the distribution of dFDA Average Total Timeline Shift across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Average Total Timeline Shift

This exceedance probability chart shows the likelihood that dFDA Average Total Timeline Shift will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Treatment Timeline Acceleration: 204 years

📊 Details

Years earlier the average first treatment arrives due to dFDA’s trial capacity increase. Calculated as the status quo timeline reduced by the inverse of the capacity multiplier. Uses only trial capacity multiplier (not combined with valley of death rescue) because additional candidates don’t directly speed therapeutic space exploration.

Inputs:

Status Quo Average Years to First Treatment 🔢: 222 years
Trial Capacity Multiplier 🔢: 12.3x

\[ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Treatment Timeline Acceleration

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Status Quo Avg Years To First Treatment	1.0665	Strong driver
dFDA Trial Capacity Multiplier	-0.0779	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: dFDA Treatment Timeline Acceleration

Statistic	Value
Baseline (deterministic)	204
Mean (expected value)	225
Median (50th percentile)	223
Standard Deviation	62.3
90% Confidence Interval	[123, 350]

The histogram shows the distribution of dFDA Treatment Timeline Acceleration across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Treatment Timeline Acceleration

This exceedance probability chart shows the likelihood that dFDA Treatment Timeline Acceleration will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Trial Cost Reduction Factor: 44.1x

📊 Details

Cost reduction factor projected for dFDA pragmatic trials ($41K traditional / $1,200 dFDA = 34x)

Inputs:

Phase 3 Cost per Patient 📊: $41K (95% CI: $20K - $120K)
dFDA Pragmatic Trial Cost per Patient 📊: $929 (95% CI: $97 - $3K)

\[ \begin{gathered} k_{reduce} \\ = \frac{Cost_{P3,pt}}{Cost_{pragmatic,pt}} \\ = \frac{\$41K}{\$929} \\ = 44.1 \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Trial Cost Reduction Factor

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Pragmatic Trial Cost Per Patient	-8.8326	Strong driver
Traditional Phase3 Cost Per Patient	8.3341	Strong driver

Monte Carlo Distribution

Simulation Results Summary: dFDA Trial Cost Reduction Factor

Statistic	Value
Baseline (deterministic)	44.1x
Mean (expected value)	52.8x
Median (50th percentile)	48x
Standard Deviation	19.5x
90% Confidence Interval	[39.4x, 89.1x]

The histogram shows the distribution of dFDA Trial Cost Reduction Factor across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Trial Cost Reduction Factor

This exceedance probability chart shows the likelihood that dFDA Trial Cost Reduction Factor will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Trial Cost Reduction Percentage: 97.7%

📊 Details

Trial cost reduction percentage: (traditional - dFDA) / traditional = ($41K - $1.2K) / $41K = 97%

Inputs:

dFDA Pragmatic Trial Cost per Patient 📊: $929 (95% CI: $97 - $3K)
Phase 3 Cost per Patient 📊: $41K (95% CI: $20K - $120K)

\[ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Trial Cost Reduction Percentage

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Pragmatic Trial Cost Per Patient	-6.4207	Strong driver
Traditional Phase3 Cost Per Patient	5.6539	Strong driver

Monte Carlo Distribution

Simulation Results Summary: dFDA Trial Cost Reduction Percentage

Statistic	Value
Baseline (deterministic)	97.7%
Mean (expected value)	98%
Median (50th percentile)	97.9%
Standard Deviation	0.401%
90% Confidence Interval	[97.5%, 98.9%]

The histogram shows the distribution of dFDA Trial Cost Reduction Percentage across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Trial Cost Reduction Percentage

This exceedance probability chart shows the likelihood that dFDA Trial Cost Reduction Percentage will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Valley of Death Rescue Multiplier: 1.4x

📊 Details

Factor increase in drugs entering development when dFDA eliminates Phase 2/3 cost barrier. Valley-of-death attrition (40%) becomes new drugs, so 1 + 0.40 = 1.4× more drugs.

Inputs:

Valley of Death Attrition Rate 📊: 40% (95% CI: 25% - 55%)

\[ k_{rescue} = Attrition_{valley} + 1 = 40\% + 1 = 1.4 \]

~ Medium confidence

Sensitivity Analysis

Monte Carlo Distribution

Simulation Results Summary: dFDA Valley of Death Rescue Multiplier

Statistic	Value
Baseline (deterministic)	1.4x
Mean (expected value)	1.4x
Median (50th percentile)	1.4x
Standard Deviation	2.22e-16x
90% Confidence Interval	[1.4x, 1.4x]

The histogram shows the distribution of dFDA Valley of Death Rescue Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Valley of Death Rescue Multiplier

This exceedance probability chart shows the likelihood that dFDA Valley of Death Rescue Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Patients Fundable Annually: 23.4M patients/year

📊 Details

Number of patients fundable annually at dFDA pragmatic trial cost ($1,200/patient). Based on empirical pragmatic trial costs (RECOVERY to PCORnet range).

Inputs:

Annual Clinical Trial Patient Subsidies 🔢: $21.7B
dFDA Pragmatic Trial Cost per Patient 📊: $929 (95% CI: $97 - $3K)

\[ \begin{gathered} N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Patients Fundable Annually

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
DIH Treasury Trial Subsidies Annual	2.3351	Strong driver
dFDA Pragmatic Trial Cost Per Patient	1.5755	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Patients Fundable Annually

Statistic	Value
Baseline (deterministic)	23.4M
Mean (expected value)	38.6M
Median (50th percentile)	30.2M
Standard Deviation	30.2M
90% Confidence Interval	[9.44M, 96.8M]

The histogram shows the distribution of Patients Fundable Annually across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Patients Fundable Annually

This exceedance probability chart shows the likelihood that Patients Fundable Annually will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Medical Research Percentage of Treaty Funding: 80%

📊 Details

Percentage of treaty funding allocated to medical research (after bond payouts and IAB incentives)

Inputs:

Annual Funding for Pragmatic Clinical Trials 🔢: $21.8B
Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $27.2B

\[ \begin{gathered} Pct_{treasury,RD} = \frac{Treasury_{RD,ann}}{Funding_{treaty}} = \frac{\$21.8B}{\$27.2B} = 80\% \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Monte Carlo Distribution

Simulation Results Summary: Medical Research Percentage of Treaty Funding

Statistic	Value
Baseline (deterministic)	80%
Mean (expected value)	80%
Median (50th percentile)	80%
Standard Deviation	1.11e-14%
90% Confidence Interval	[80%, 80%]

The histogram shows the distribution of Medical Research Percentage of Treaty Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Medical Research Percentage of Treaty Funding

This exceedance probability chart shows the likelihood that Medical Research Percentage of Treaty Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Annual Funding for Pragmatic Clinical Trials: $21.8B

📊 Details

Annual funding for pragmatic clinical trials (treaty funding minus VICTORY Incentive Alignment Bond payouts and IAB political incentive mechanism)

Inputs:

Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $27.2B
Annual VICTORY Incentive Alignment Bond Payout 🔢: $2.72B
Annual IAB Political Incentive Funding 🔢: $2.72B

\[ \begin{gathered} Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Annual Clinical Trial Patient Subsidies: $21.7B

📊 Details

Annual clinical trial patient subsidies (all medical research funds after Decentralized Framework for Drug Assessment operations)

Inputs:

Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M
Annual Funding for Pragmatic Clinical Trials 🔢: $21.8B

\[ \begin{gathered} Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Annual Clinical Trial Patient Subsidies

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Annual OPEX	-1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Annual Clinical Trial Patient Subsidies

Statistic	Value
Baseline (deterministic)	$21.7B
Mean (expected value)	$21.7B
Median (50th percentile)	$21.7B
Standard Deviation	$8.21M
90% Confidence Interval	[$21.7B, $21.7B]

The histogram shows the distribution of Annual Clinical Trial Patient Subsidies across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Annual Clinical Trial Patient Subsidies

This exceedance probability chart shows the likelihood that Annual Clinical Trial Patient Subsidies will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Patient Trial Subsidies Percentage of Treaty Funding: 79.9%

📊 Details

Percentage of treaty funding going directly to patient trial subsidies

Inputs:

Annual Clinical Trial Patient Subsidies 🔢: $21.7B
Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $27.2B

\[ \begin{gathered} Pct_{subsidies} = \frac{Subsidies_{trial,ann}}{Funding_{treaty}} = \frac{\$21.7B}{\$27.2B} = 79.9\% \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Patient Trial Subsidies Percentage of Treaty Funding

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
DIH Treasury Trial Subsidies Annual	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Patient Trial Subsidies Percentage of Treaty Funding

Statistic	Value
Baseline (deterministic)	79.9%
Mean (expected value)	79.9%
Median (50th percentile)	79.9%
Standard Deviation	0.0302%
90% Confidence Interval	[79.8%, 79.9%]

The histogram shows the distribution of Patient Trial Subsidies Percentage of Treaty Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Patient Trial Subsidies Percentage of Treaty Funding

This exceedance probability chart shows the likelihood that Patient Trial Subsidies Percentage of Treaty Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Diseases Without Effective Treatment: 6.65k diseases

📊 Details

Number of diseases without effective treatment. 95% of 7,000 rare diseases lack FDA-approved treatment (per Orphanet 2024). This represents the therapeutic search space that remains unexplored.

Inputs:

Total Number of Rare Diseases Globally 📊: 7.00k diseases (95% CI: 6.00k diseases - 10.0k diseases)

\[ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} \]

Methodology:¹³⁸

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Diseases Without Effective Treatment

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Rare Diseases Count Global	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Diseases Without Effective Treatment

Statistic	Value
Baseline (deterministic)	6.65k
Mean (expected value)	6.73k
Median (50th percentile)	6.64k
Standard Deviation	835
90% Confidence Interval	[5.70k, 8.24k]

The histogram shows the distribution of Diseases Without Effective Treatment across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Diseases Without Effective Treatment

This exceedance probability chart shows the likelihood that Diseases Without Effective Treatment will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths: 18.4k:1

📊 Details

Ratio of annual disease deaths to 9/11 terrorism deaths

Inputs:

Annual Deaths from All Diseases and Aging Globally 📊: 55.0M deaths/year (SE: ±5.00M deaths/year)
Deaths from 9/11 Terrorist Attacks 📊: 3.00k deaths

\[ \begin{gathered} Ratio_{dis:terror} \\ = \frac{Deaths_{curable,ann}}{Deaths_{9/11}} \\ = \frac{55M}{3{,}000} \\ = 18{,}400 \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual Deaths Curable Diseases	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths

Statistic	Value
Baseline (deterministic)	18.4k:1
Mean (expected value)	18.3k:1
Median (50th percentile)	18.3k:1
Standard Deviation	1.68k:1
90% Confidence Interval	[15.6k:1, 21.1k:1]

The histogram shows the distribution of Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths

This exceedance probability chart shows the likelihood that Ratio of Annual Disease Deaths to 9/11 Terrorism Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Ratio of Annual Disease Deaths to War Deaths: 225:1

📊 Details

Ratio of annual disease deaths to war deaths

Inputs:

Annual Deaths from All Diseases and Aging Globally 📊: 55.0M deaths/year (SE: ±5.00M deaths/year)
Total Annual Conflict Deaths Globally 🔢: 245k deaths/year

\[ \begin{gathered} Ratio_{dis:war} = \frac{Deaths_{curable,ann}}{Deaths_{conflict}} = \frac{55M}{245{,}000} = 225 \\[0.5em] \text{where } Deaths_{conflict} \\ = Deaths_{combat} + Deaths_{state} + Deaths_{terror} \\ = 234{,}000 + 2{,}700 + 8{,}300 \\ = 245{,}000 \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Ratio of Annual Disease Deaths to War Deaths

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual Conflict Deaths Total	-2.9115	Strong driver
Global Annual Deaths Curable Diseases	1.9792	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Ratio of Annual Disease Deaths to War Deaths

Statistic	Value
Baseline (deterministic)	225:1
Mean (expected value)	226:1
Median (50th percentile)	227:1
Standard Deviation	8.8:1
90% Confidence Interval	[210:1, 239:1]

The histogram shows the distribution of Ratio of Annual Disease Deaths to War Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Ratio of Annual Disease Deaths to War Deaths

This exceedance probability chart shows the likelihood that Ratio of Annual Disease Deaths to War Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Drug Cost Increase: 1980s to Current: 13.4x

📊 Details

Drug development cost increase from 1980s to current ($194M → $2.6B = 13.4x)

Inputs:

Drug Development Cost (1980s) 📊: $194M (95% CI: $146M - $242M)
Pharma Drug Development Cost (Current System) 📊: $2.60B (95% CI: $1.50B - $4B)

\[ \begin{gathered} k_{cost,80s} \\ = \frac{Cost_{dev,curr}}{Cost_{dev,80s}} \\ = \frac{\$2.6B}{\$194M} \\ = 13.4 \end{gathered} \]

Methodology:³³

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Drug Cost Increase: 1980s to Current

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Pharma Drug Development Cost Current	1.6909	Strong driver
Drug Development Cost 1980s	-0.7048	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Drug Cost Increase: 1980s to Current

Statistic	Value
Baseline (deterministic)	13.4x
Mean (expected value)	13.3x
Median (50th percentile)	13.3x
Standard Deviation	0.915x
90% Confidence Interval	[11.9x, 14.7x]

The histogram shows the distribution of Drug Cost Increase: 1980s to Current across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Drug Cost Increase: 1980s to Current

This exceedance probability chart shows the likelihood that Drug Cost Increase: 1980s to Current will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Drug Cost Increase: Pre-1962 to Current: 105x

📊 Details

Drug development cost increase from pre-1962 to current ($24.7M → $2.6B = 105×)

Inputs:

Pharma Drug Development Cost (Current System) 📊: $2.60B (95% CI: $1.50B - $4B)
Pre-1962 Drug Development Cost (2024 Dollars) 📊: $24.7M (95% CI: $19.5M - $30M)

\[ \begin{gathered} k_{cost,pre62} \\ = \frac{Cost_{dev,curr}}{Cost_{pre62,24}} \\ = \frac{\$2.6B}{\$24.7M} \\ = 105 \end{gathered} \]

Methodology:⁹⁴

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Drug Cost Increase: Pre-1962 to Current

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Pharma Drug Development Cost Current	1.3110	Strong driver
Pre 1962 Drug Development Cost 2024 Usd	-0.3181	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Drug Cost Increase: Pre-1962 to Current

Statistic	Value
Baseline (deterministic)	105x
Mean (expected value)	104x
Median (50th percentile)	104x
Standard Deviation	9.03x
90% Confidence Interval	[90.6x, 119x]

The histogram shows the distribution of Drug Cost Increase: Pre-1962 to Current across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Drug Cost Increase: Pre-1962 to Current

This exceedance probability chart shows the likelihood that Drug Cost Increase: Pre-1962 to Current will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Possible Drug-Disease Combinations: 9.50M combinations

📊 Details

Total possible drug-disease combinations using existing safe compounds

Inputs:

Safe Compounds Available for Testing: 9.50k compounds (95% CI: 7.00k compounds - 12.0k compounds)
Trial-Relevant Diseases: 1.00k diseases (95% CI: 800 diseases - 1.20k diseases)

\[ \begin{gathered} N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Therapeutic Frontier Exploration Ratio: 0.342%

📊 Details

Fraction of possible drug-disease space actually tested (<1%)

Inputs:

Tested Drug-Disease Relationships: 32.5k relationships (95% CI: 15.0k relationships - 50.0k relationships)
Possible Drug-Disease Combinations 🔢: 9.50M combinations

\[ \begin{gathered} Ratio_{explore} = \frac{N_{tested}}{N_{combos}} = \frac{32{,}500}{9.5M} = 0.342\% \\[0.5em] \text{where } N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Therapeutic Frontier Exploration Ratio

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Tested Relationships Estimate	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Therapeutic Frontier Exploration Ratio

Statistic	Value
Baseline (deterministic)	0.342%
Mean (expected value)	0.339%
Median (50th percentile)	0.329%
Standard Deviation	0.0868%
90% Confidence Interval	[0.21%, 0.514%]

The histogram shows the distribution of Therapeutic Frontier Exploration Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Therapeutic Frontier Exploration Ratio

This exceedance probability chart shows the likelihood that Therapeutic Frontier Exploration Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Conflict Deaths Globally: 245k deaths/year

📊 Details

Total annual conflict deaths globally (sum of combat, terror, state violence)

Inputs:

Annual Deaths from Active Combat Worldwide 📊: 234k deaths/year (95% CI: 180k deaths/year - 300k deaths/year)
Annual Deaths from State Violence 📊: 2.70k deaths/year (95% CI: 1.50k deaths/year - 5.00k deaths/year)
Annual Deaths from Terror Attacks Globally 📊: 8.30k deaths/year (95% CI: 6.00k deaths/year - 12.0k deaths/year)

\[ \begin{gathered} Deaths_{conflict} \\ = Deaths_{combat} + Deaths_{state} + Deaths_{terror} \\ = 234{,}000 + 2{,}700 + 8{,}300 \\ = 245{,}000 \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Conflict Deaths Globally

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual Conflict Deaths Active Combat	0.9276	Strong driver
Global Annual Conflict Deaths Terror Attacks	0.0461	Minimal effect
Global Annual Conflict Deaths State Violence	0.0266	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Annual Conflict Deaths Globally

Statistic	Value
Baseline (deterministic)	245k
Mean (expected value)	244k
Median (50th percentile)	242k
Standard Deviation	31.5k
90% Confidence Interval	[194k, 302k]

The histogram shows the distribution of Total Annual Conflict Deaths Globally across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Conflict Deaths Globally

This exceedance probability chart shows the likelihood that Total Annual Conflict Deaths Globally will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Cost of War Worldwide: $11.4T

📊 Details

Total annual cost of war worldwide (direct + indirect costs)

Inputs:

Total Annual Direct War Costs 🔢: $7.66T
Total Annual Indirect War Costs 🔢: $3.70T

\[ \begin{gathered} Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \\[0.5em] \text{where } Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \\[0.5em] \text{where } Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \\[0.5em] \text{where } Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \\[0.5em] \text{where } Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \\[0.5em] \text{where } Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \\[0.5em] \text{where } Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} \\ + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \\[0.5em] \text{where } Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \\[0.5em] \text{where } Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} \\ + Loss_{capital,conflict} + Cost_{psych} \\ + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Cost of War Worldwide

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual War Direct Costs Total	0.6553	Strong driver
Global Annual War Indirect Costs Total	0.4150	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Total Annual Cost of War Worldwide

Statistic	Value
Baseline (deterministic)	$11.4T
Mean (expected value)	$11.3T
Median (50th percentile)	$11.2T
Standard Deviation	$1.51T
90% Confidence Interval	[$9.01T, $14.1T]

The histogram shows the distribution of Total Annual Cost of War Worldwide across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Cost of War Worldwide

This exceedance probability chart shows the likelihood that Total Annual Cost of War Worldwide will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Annual Cost of Combat Deaths: $2.34T

📊 Details

Annual cost of combat deaths (deaths × VSL)

Inputs:

Annual Deaths from Active Combat Worldwide 📊: 234k deaths/year (95% CI: 180k deaths/year - 300k deaths/year)
Value of Statistical Life 📊: $10M (95% CI: $5M - $15M)

\[ \begin{gathered} Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Annual Cost of Combat Deaths

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Value Of Statistical Life	0.9096	Strong driver
Global Annual Conflict Deaths Active Combat	0.4115	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Annual Cost of Combat Deaths

Statistic	Value
Baseline (deterministic)	$2.34T
Mean (expected value)	$2.31T
Median (50th percentile)	$2.24T
Standard Deviation	$703B
90% Confidence Interval	[$1.25T, $3.57T]

The histogram shows the distribution of Annual Cost of Combat Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Annual Cost of Combat Deaths

This exceedance probability chart shows the likelihood that Annual Cost of Combat Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Annual Cost of State Violence Deaths: $27B

📊 Details

Annual cost of state violence deaths (deaths × VSL)

Inputs:

Annual Deaths from State Violence 📊: 2.70k deaths/year (95% CI: 1.50k deaths/year - 5.00k deaths/year)
Value of Statistical Life 📊: $10M (95% CI: $5M - $15M)

\[ \begin{gathered} Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Annual Cost of State Violence Deaths

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual Conflict Deaths State Violence	0.7358	Strong driver
Value Of Statistical Life	0.6553	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Annual Cost of State Violence Deaths

Statistic	Value
Baseline (deterministic)	$27B
Mean (expected value)	$26.6B
Median (50th percentile)	$24.5B
Standard Deviation	$11.3B
90% Confidence Interval	[$12B, $48.4B]

The histogram shows the distribution of Annual Cost of State Violence Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Annual Cost of State Violence Deaths

This exceedance probability chart shows the likelihood that Annual Cost of State Violence Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Annual Cost of Terror Deaths: $83B

📊 Details

Annual cost of terror deaths (deaths × VSL)

Inputs:

Annual Deaths from Terror Attacks Globally 📊: 8.30k deaths/year (95% CI: 6.00k deaths/year - 12.0k deaths/year)
Value of Statistical Life 📊: $10M (95% CI: $5M - $15M)

\[ \begin{gathered} Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Annual Cost of Terror Deaths

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Value Of Statistical Life	0.8410	Strong driver
Global Annual Conflict Deaths Terror Attacks	0.5319	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Annual Cost of Terror Deaths

Statistic	Value
Baseline (deterministic)	$83B
Mean (expected value)	$82.1B
Median (50th percentile)	$78.9B
Standard Deviation	$27B
90% Confidence Interval	[$43.1B, $131B]

The histogram shows the distribution of Annual Cost of Terror Deaths across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Annual Cost of Terror Deaths

This exceedance probability chart shows the likelihood that Annual Cost of Terror Deaths will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Human Life Losses from Conflict: $2.45T

📊 Details

Total annual human life losses from conflict (sum of combat, terror, state violence)

Inputs:

Annual Cost of Combat Deaths 🔢: $2.34T
Annual Cost of State Violence Deaths 🔢: $27B
Annual Cost of Terror Deaths 🔢: $83B

\[ \begin{gathered} Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \\[0.5em] \text{where } Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \\[0.5em] \text{where } Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \\[0.5em] \text{where } Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Human Life Losses from Conflict

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual Human Cost Active Combat	0.9500	Strong driver
Global Annual Human Cost Terror Attacks	0.0365	Minimal effect
Global Annual Human Cost State Violence	0.0152	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Annual Human Life Losses from Conflict

Statistic	Value
Baseline (deterministic)	$2.45T
Mean (expected value)	$2.42T
Median (50th percentile)	$2.35T
Standard Deviation	$740B
90% Confidence Interval	[$1.31T, $3.75T]

The histogram shows the distribution of Total Annual Human Life Losses from Conflict across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Human Life Losses from Conflict

This exceedance probability chart shows the likelihood that Total Annual Human Life Losses from Conflict will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Infrastructure Destruction: $1.88T

📊 Details

Total annual infrastructure destruction (sum of transportation, energy, communications, water, education, healthcare)

Inputs:

Annual Infrastructure Damage to Communications from Conflict 📊: $298B (95% CI: $209B - $418B)
Annual Infrastructure Damage to Education Facilities from Conflict 📊: $234B (95% CI: $164B - $328B)
Annual Infrastructure Damage to Energy Systems from Conflict 📊: $422B (95% CI: $295B - $590B)
Annual Infrastructure Damage to Healthcare Facilities from Conflict 📊: $166B (95% CI: $116B - $232B)
Annual Infrastructure Damage to Transportation from Conflict 📊: $487B (95% CI: $340B - $680B)
Annual Infrastructure Damage to Water Systems from Conflict 📊: $268B (95% CI: $187B - $375B)

\[ \begin{gathered} Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Infrastructure Destruction

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual Infrastructure Damage Transportation Conflict	0.2591	Weak driver
Global Annual Infrastructure Damage Energy Conflict	0.2249	Weak driver
Global Annual Infrastructure Damage Communications Conflict	0.1593	Weak driver
Global Annual Infrastructure Damage Water Conflict	0.1433	Weak driver
Global Annual Infrastructure Damage Education Conflict	0.1250	Weak driver
Global Annual Infrastructure Damage Healthcare Conflict	0.0884	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Annual Infrastructure Destruction

Statistic	Value
Baseline (deterministic)	$1.88T
Mean (expected value)	$1.87T
Median (50th percentile)	$1.84T
Standard Deviation	$319B
90% Confidence Interval	[$1.37T, $2.47T]

The histogram shows the distribution of Total Annual Infrastructure Destruction across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Infrastructure Destruction

This exceedance probability chart shows the likelihood that Total Annual Infrastructure Destruction will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Trade Disruption: $616B

📊 Details

Total annual trade disruption (sum of shipping, supply chain, energy prices, currency instability)

Inputs:

Annual Trade Disruption Costs from Currency Instability 📊: $57.4B (95% CI: $40B - $80B)
Annual Trade Disruption Costs from Energy Price Volatility 📊: $125B (95% CI: $87B - $175B)
Annual Trade Disruption Costs from Shipping Disruptions 📊: $247B (95% CI: $173B - $346B)
Annual Trade Disruption Costs from Supply Chain Disruptions 📊: $187B (95% CI: $131B - $262B)

\[ \begin{gathered} Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Trade Disruption

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual Trade Disruption Shipping Conflict	0.4005	Moderate driver
Global Annual Trade Disruption Supply Chain Conflict	0.3033	Moderate driver
Global Annual Trade Disruption Energy Price Conflict	0.2037	Weak driver
Global Annual Trade Disruption Currency Conflict	0.0926	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Annual Trade Disruption

Statistic	Value
Baseline (deterministic)	$616B
Mean (expected value)	$614B
Median (50th percentile)	$605B
Standard Deviation	$105B
90% Confidence Interval	[$450B, $812B]

The histogram shows the distribution of Total Annual Trade Disruption across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Trade Disruption

This exceedance probability chart shows the likelihood that Total Annual Trade Disruption will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Direct War Costs: $7.66T

📊 Details

Total annual direct war costs (military spending + infrastructure + human life + trade disruption)

Inputs:

Total Annual Human Life Losses from Conflict 🔢: $2.45T
Total Annual Infrastructure Destruction 🔢: $1.88T
Total Annual Trade Disruption 🔢: $616B
Global Military Spending in 2024 📊: $2.72T

\[ \begin{gathered} Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \\[0.5em] \text{where } Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \\[0.5em] \text{where } Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \\[0.5em] \text{where } Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \\[0.5em] \text{where } Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \\[0.5em] \text{where } Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} \\ + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \\[0.5em] \text{where } Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Direct War Costs

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual Human Life Losses Conflict	0.7463	Strong driver
Global Annual Infrastructure Destruction Conflict	0.3211	Moderate driver
Global Annual Trade Disruption Conflict	0.1057	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Total Annual Direct War Costs

Statistic	Value
Baseline (deterministic)	$7.66T
Mean (expected value)	$7.62T
Median (50th percentile)	$7.53T
Standard Deviation	$992B
90% Confidence Interval	[$6.14T, $9.40T]

The histogram shows the distribution of Total Annual Direct War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Direct War Costs

This exceedance probability chart shows the likelihood that Total Annual Direct War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Indirect War Costs: $3.70T

📊 Details

Total annual indirect war costs (opportunity cost + veterans + refugees + environment + mental health + lost productivity)

Inputs:

Annual Environmental Damage and Restoration Costs from Conflict 📊: $100B (95% CI: $70B - $140B)
Annual Lost Economic Growth from Military Spending Opportunity Cost 📊: $2.72T (95% CI: $1.90T - $3.80T)
Annual Lost Productivity from Conflict Casualties 📊: $300B (95% CI: $210B - $420B)
Annual PTSD and Mental Health Costs from Conflict 📊: $232B (95% CI: $162B - $325B)
Annual Refugee Support Costs 📊: $150B (95% CI: $105B - $210B)
Annual Veteran Healthcare Costs 📊: $200B (95% CI: $140B - $280B)

\[ \begin{gathered} Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} + Loss_{capital,conflict} \\ + Cost_{psych} + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Indirect War Costs

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual Refugee Support Costs	3.5996	Strong driver
Global Annual Lost Human Capital Conflict	-1.9754	Strong driver
Global Annual Environmental Damage Conflict	-1.4754	Strong driver
Global Annual Lost Economic Growth Military Spending	0.7342	Strong driver
Global Annual Psychological Impact Costs Conflict	0.0630	Minimal effect
Global Annual Veteran Healthcare Costs	0.0541	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Annual Indirect War Costs

Statistic	Value
Baseline (deterministic)	$3.70T
Mean (expected value)	$3.69T
Median (50th percentile)	$3.63T
Standard Deviation	$628B
90% Confidence Interval	[$2.71T, $4.87T]

The histogram shows the distribution of Total Annual Indirect War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Indirect War Costs

This exceedance probability chart shows the likelihood that Total Annual Indirect War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Economic Burden of Disease Globally: $109T

📊 Details

Total economic burden of disease globally (medical + productivity + mortality)

Inputs:

Global Annual Direct Medical Costs of Disease 📊: $9.90T (95% CI: $7T - $14T)
Global Annual Economic Value of Human Life Lost to Disease 📊: $94.2T (95% CI: $66T - $132T)
Global Annual Productivity Loss from Disease 📊: $5T (95% CI: $3.50T - $7T)

\[ \begin{gathered} Burden_{disease} \\ = Cost_{medical,direct} + Loss_{life,disease} \\ + Loss_{productivity} \\ = \$9.9T + \$94.2T + \$5T \\ = \$109T \end{gathered} \]

Methodology:⁵⁶

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Economic Burden of Disease Globally

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Disease Human Life Value Loss Annual	0.8628	Strong driver
Global Disease Direct Medical Cost Annual	0.0915	Minimal effect
Global Disease Productivity Loss Annual	0.0458	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Economic Burden of Disease Globally

Statistic	Value
Baseline (deterministic)	$109T
Mean (expected value)	$109T
Median (50th percentile)	$107T
Standard Deviation	$18.6T
90% Confidence Interval	[$79.8T, $144T]

The histogram shows the distribution of Total Economic Burden of Disease Globally across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Economic Burden of Disease Globally

This exceedance probability chart shows the likelihood that Total Economic Burden of Disease Globally will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Cost of War and Disease with All Externalities: $129T

📊 Details

Total annual cost of war and disease with all externalities (direct + indirect costs for both)

Inputs:

Total Annual Cost of War Worldwide 🔢: $11.4T
Total Economic Burden of Disease Globally 🔢: $109T
Annual Global Spending on Symptomatic Disease Treatment 📊: $8.20T (95% CI: $6.50T - $10T)

\[ \begin{gathered} Cost_{health+war} \\ = Cost_{war,total} + Burden_{disease} + Spending_{symptom} \\ = \$11.4T + \$109T + \$8.2T \\ = \$129T \\[0.5em] \text{where } Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \\[0.5em] \text{where } Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \\[0.5em] \text{where } Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \\[0.5em] \text{where } Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \\[0.5em] \text{where } Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \\[0.5em] \text{where } Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \\[0.5em] \text{where } Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} \\ + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \\[0.5em] \text{where } Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \\[0.5em] \text{where } Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} \\ + Loss_{capital,conflict} + Cost_{psych} \\ + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \\[0.5em] \text{where } Burden_{disease} \\ = Cost_{medical,direct} + Loss_{life,disease} \\ + Loss_{productivity} \\ = \$9.9T + \$94.2T + \$5T \\ = \$109T \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Cost of War and Disease with All Externalities

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Disease Economic Burden Annual	0.8934	Strong driver
Global Annual Direct Indirect War Cost	0.0728	Minimal effect
Global Symptomatic Disease Treatment Annual	0.0410	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Annual Cost of War and Disease with All Externalities

Statistic	Value
Baseline (deterministic)	$129T
Mean (expected value)	$128T
Median (50th percentile)	$126T
Standard Deviation	$20.8T
90% Confidence Interval	[$95.8T, $168T]

The histogram shows the distribution of Total Annual Cost of War and Disease with All Externalities across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Cost of War and Disease with All Externalities

This exceedance probability chart shows the likelihood that Total Annual Cost of War and Disease with All Externalities will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Healthcare vs Military Multiplier Ratio: 7.17x

📊 Details

Ratio of healthcare to military fiscal multipliers. Healthcare investment generates 7× more economic activity per dollar than military spending.

Inputs:

Economic Multiplier for Healthcare Investment 📊: 4.3x (95% CI: 3x - 6x)
Economic Multiplier for Military Spending 📊: 0.6x (95% CI: 0.4x - 0.9x)

\[ \begin{gathered} r_{health/mil} \\ = \frac{k_{health}}{k_{mil}} \\ = \frac{4.3}{0.6} \\ = 7.17 \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Healthcare vs Military Multiplier Ratio

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Economic Multiplier Military Spending	-0.5163	Strong driver
Economic Multiplier Healthcare Investment	-0.4760	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Healthcare vs Military Multiplier Ratio

Statistic	Value
Baseline (deterministic)	7.17x
Mean (expected value)	7.21x
Median (50th percentile)	7.22x
Standard Deviation	0.227x
90% Confidence Interval	[6.83x, 7.57x]

The histogram shows the distribution of Healthcare vs Military Multiplier Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Healthcare vs Military Multiplier Ratio

This exceedance probability chart shows the likelihood that Healthcare vs Military Multiplier Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Annual IAB Political Incentive Funding: $2.72B

📊 Details

Annual funding for IAB political incentive mechanism (independent expenditures supporting high-scoring politicians, post-office fellowship endowments, Public Good Score infrastructure)

Inputs:

Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $27.2B
IAB Political Incentive Funding Percentage: 10%

\[ \begin{gathered} Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Medical Research Spending as Percentage of Total Disease Burden: 0.0525%

📊 Details

Medical research spending as percentage of total disease burden

Inputs:

Global Government Medical Research Spending 📊: $67.5B (95% CI: $54B - $81B)
Total Annual Cost of War and Disease with All Externalities 🔢: $129T

\[ \begin{gathered} Pct_{RD:burden} \\ = \frac{Spending_{RD}}{Cost_{health+war}} \\ = \frac{\$67.5B}{\$129T} \\ = 0.0525\% \\[0.5em] \text{where } Cost_{health+war} \\ = Cost_{war,total} + Burden_{disease} + Spending_{symptom} \\ = \$11.4T + \$109T + \$8.2T \\ = \$129T \\[0.5em] \text{where } Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \\[0.5em] \text{where } Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \\[0.5em] \text{where } Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \\[0.5em] \text{where } Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \\[0.5em] \text{where } Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \\[0.5em] \text{where } Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \\[0.5em] \text{where } Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} \\ + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \\[0.5em] \text{where } Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \\[0.5em] \text{where } Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} \\ + Loss_{capital,conflict} + Cost_{psych} \\ + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \\[0.5em] \text{where } Burden_{disease} \\ = Cost_{medical,direct} + Loss_{life,disease} \\ + Loss_{productivity} \\ = \$9.9T + \$94.2T + \$5T \\ = \$109T \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Medical Research Spending as Percentage of Total Disease Burden

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Total Health And War Cost Annual	-0.5152	Strong driver
Global Med Research Spending	-0.4795	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Medical Research Spending as Percentage of Total Disease Burden

Statistic	Value
Baseline (deterministic)	0.0525%
Mean (expected value)	0.053%
Median (50th percentile)	0.053%
Standard Deviation	0.00337%
90% Confidence Interval	[0.0474%, 0.0588%]

The histogram shows the distribution of Medical Research Spending as Percentage of Total Disease Burden across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Medical Research Spending as Percentage of Total Disease Burden

This exceedance probability chart shows the likelihood that Medical Research Spending as Percentage of Total Disease Burden will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Ratio of Military to Government Clinical Trials Spending: 604:1

📊 Details

Ratio of global military spending to government clinical trials spending

Inputs:

Global Military Spending in 2024 📊: $2.72T
Annual Global Government Spending on Clinical Trials 📊: $4.50B (95% CI: $3B - $6B)

\[ \begin{gathered} Ratio_{mil:gov} \\ = \frac{Spending_{mil}}{Spending_{trials,gov}} \\ = \frac{\$2.72T}{\$4.5B} \\ = 604 \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Ratio of Military to Government Clinical Trials Spending

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Government Clinical Trials Spending Annual	-0.9786	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Ratio of Military to Government Clinical Trials Spending

Statistic	Value
Baseline (deterministic)	604:1
Mean (expected value)	635:1
Median (50th percentile)	621:1
Standard Deviation	127:1
90% Confidence Interval	[453:1, 894:1]

The histogram shows the distribution of Ratio of Military to Government Clinical Trials Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Ratio of Military to Government Clinical Trials Spending

This exceedance probability chart shows the likelihood that Ratio of Military to Government Clinical Trials Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Ratio of Military Spending to Medical Research Spending: 40.3:1

📊 Details

Ratio of military spending to medical research spending

Inputs:

Global Government Medical Research Spending 📊: $67.5B (95% CI: $54B - $81B)
Global Military Spending in 2024 📊: $2.72T

\[ \begin{gathered} Ratio_{mil:RD} \\ = \frac{Spending_{mil}}{Spending_{RD}} \\ = \frac{\$2.72T}{\$67.5B} \\ = 40.3 \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Ratio of Military Spending to Medical Research Spending

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Med Research Spending	-0.9931	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Ratio of Military Spending to Medical Research Spending

Statistic	Value
Baseline (deterministic)	40.3:1
Mean (expected value)	40.8:1
Median (50th percentile)	40.6:1
Standard Deviation	4:1
90% Confidence Interval	[34.3:1, 48:1]

The histogram shows the distribution of Ratio of Military Spending to Medical Research Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Ratio of Military Spending to Medical Research Spending

This exceedance probability chart shows the likelihood that Ratio of Military Spending to Medical Research Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Annual Peace Dividend from 1% Reduction in Total War Costs: $114B

📊 Details

Annual peace dividend from 1% reduction in total war costs (theoretical maximum at ε=1.0)

Inputs:

Total Annual Cost of War Worldwide 🔢: $11.4T
1% Reduction in Military Spending/War Costs from Treaty: 1%

\[ \begin{gathered} Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \\[0.5em] \text{where } Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \\[0.5em] \text{where } Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \\[0.5em] \text{where } Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \\[0.5em] \text{where } Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \\[0.5em] \text{where } Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \\[0.5em] \text{where } Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \\[0.5em] \text{where } Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} \\ + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \\[0.5em] \text{where } Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \\[0.5em] \text{where } Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} \\ + Loss_{capital,conflict} + Cost_{psych} \\ + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Annual Peace Dividend from 1% Reduction in Total War Costs

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Annual Direct Indirect War Cost	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Annual Peace Dividend from 1% Reduction in Total War Costs

Statistic	Value
Baseline (deterministic)	$114B
Mean (expected value)	$113B
Median (50th percentile)	$112B
Standard Deviation	$15.1B
90% Confidence Interval	[$90.1B, $141B]

The histogram shows the distribution of Annual Peace Dividend from 1% Reduction in Total War Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Annual Peace Dividend from 1% Reduction in Total War Costs

This exceedance probability chart shows the likelihood that Annual Peace Dividend from 1% Reduction in Total War Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Conflict Reduction Benefits from 1% Less Military Spending: $86.4B

📊 Details

Conflict reduction benefits from 1% less military spending (lower confidence - assumes proportional relationship)

Inputs:

Annual Peace Dividend from 1% Reduction in Total War Costs 🔢: $114B
Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $27.2B

\[ \begin{gathered} Savings_{conflict} \\ = Benefit_{peace,soc} - Funding_{treaty} \\ = \$114B - \$27.2B \\ = \$86.4B \\[0.5em] \text{where } Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \\[0.5em] \text{where } Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \\[0.5em] \text{where } Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \\[0.5em] \text{where } Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \\[0.5em] \text{where } Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \\[0.5em] \text{where } Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \\[0.5em] \text{where } Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \\[0.5em] \text{where } Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} \\ + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \\[0.5em] \text{where } Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \\[0.5em] \text{where } Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} \\ + Loss_{capital,conflict} + Cost_{psych} \\ + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

Methodology: Direct Calculation

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Conflict Reduction Benefits from 1% Less Military Spending

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Peace Dividend Annual Societal Benefit	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Conflict Reduction Benefits from 1% Less Military Spending

Statistic	Value
Baseline (deterministic)	$86.4B
Mean (expected value)	$85.9B
Median (50th percentile)	$84.6B
Standard Deviation	$15.1B
90% Confidence Interval	[$62.9B, $113B]

The histogram shows the distribution of Conflict Reduction Benefits from 1% Less Military Spending across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Conflict Reduction Benefits from 1% Less Military Spending

This exceedance probability chart shows the likelihood that Conflict Reduction Benefits from 1% Less Military Spending will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Pragmatic Trial Cost per QALY (RECOVERY): $4.00

📊 Details

Cost per QALY for pragmatic platform trials, calculated from RECOVERY trial data. Uses global impact methodology: trial cost divided by total QALYs from downstream adoption. This measures research efficiency (discovery value), not clinical intervention ICER.

Inputs:

RECOVERY Trial Total Cost 📊: $20M (95% CI: $15M - $25M)
RECOVERY Trial Total QALYs Generated 🔢: 5.00M QALYs

\[ \begin{gathered} Cost_{pragmatic,QALY} = \frac{Cost_{RECOVERY}}{QALY_{RECOVERY}} = \frac{\$20M}{5M} = \$4 \\[0.5em] \text{where } QALY_{RECOVERY} \\ = Lives_{RECOVERY} \times QALY_{COVID} \\ = 1M \times 5 \\ = 5M \end{gathered} \]

Methodology:⁷⁷

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Pragmatic Trial Cost per QALY (RECOVERY)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Recovery Trial Total Cost	-1.4871	Strong driver
Recovery Trial Total QALYs Generated	0.5682	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Pragmatic Trial Cost per QALY (RECOVERY)

Statistic	Value
Baseline (deterministic)	$4.00
Mean (expected value)	$5.10
Median (50th percentile)	$4.55
Standard Deviation	$2.59
90% Confidence Interval	[$1.71, $10]

The histogram shows the distribution of Pragmatic Trial Cost per QALY (RECOVERY) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Pragmatic Trial Cost per QALY (RECOVERY)

This exceedance probability chart shows the likelihood that Pragmatic Trial Cost per QALY (RECOVERY) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Pragmatic Trial Efficiency Multiplier vs NIH: 12.5kx

📊 Details

How many times more cost-effective pragmatic trials are vs standard NIH research. Calculated using global impact methodology (NIH cost per QALY / pragmatic cost per QALY). Shows orders-of-magnitude efficiency gap between discovery-focused pragmatic trials and standard research.

Inputs:

NIH Standard Research Cost per QALY 📊: $50K (95% CI: $20K - $100K)
Pragmatic Trial Cost per QALY (RECOVERY) 🔢: $4.00

\[ \begin{gathered} k_{pragmatic:NIH} \\ = \frac{Cost_{NIH,QALY}}{Cost_{pragmatic,QALY}} \\ = \frac{\$50K}{\$4} \\ = 12{,}500 \\[0.5em] \text{where } Cost_{pragmatic,QALY} = \frac{Cost_{RECOVERY}}{QALY_{RECOVERY}} = \frac{\$20M}{5M} = \$4 \\[0.5em] \text{where } QALY_{RECOVERY} \\ = Lives_{RECOVERY} \times QALY_{COVID} \\ = 1M \times 5 \\ = 5M \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Pragmatic Trial Efficiency Multiplier vs NIH

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
NIH Standard Research Cost Per QALY	1.5607	Strong driver
Pragmatic Trial Cost Per QALY	0.6777	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Pragmatic Trial Efficiency Multiplier vs NIH

Statistic	Value
Baseline (deterministic)	12.5kx
Mean (expected value)	15.8kx
Median (50th percentile)	10.1kx
Standard Deviation	16.2kx
90% Confidence Interval	[2.3kx, 51.5kx]

The histogram shows the distribution of Pragmatic Trial Efficiency Multiplier vs NIH across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Pragmatic Trial Efficiency Multiplier vs NIH

This exceedance probability chart shows the likelihood that Pragmatic Trial Efficiency Multiplier vs NIH will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

RECOVERY Trial Cost Reduction Factor: 82x

📊 Details

Cost reduction factor demonstrated by RECOVERY trial ($41K traditional / $500 RECOVERY = 82x)

Inputs:

Phase 3 Cost per Patient 📊: $41K (95% CI: $20K - $120K)
Recovery Trial Cost per Patient 📊: $500 (95% CI: $400 - $2.50K)

\[ \begin{gathered} k_{RECOVERY} \\ = \frac{Cost_{P3,pt}}{Cost_{RECOVERY,pt}} \\ = \frac{\$41K}{\$500} \\ = 82 \end{gathered} \]

Methodology:⁷⁷

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for RECOVERY Trial Cost Reduction Factor

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Recovery Trial Cost Per Patient	-2.4783	Strong driver
Traditional Phase3 Cost Per Patient	2.4635	Strong driver

Monte Carlo Distribution

Simulation Results Summary: RECOVERY Trial Cost Reduction Factor

Statistic	Value
Baseline (deterministic)	82x
Mean (expected value)	71.2x
Median (50th percentile)	72.4x
Standard Deviation	15.3x
90% Confidence Interval	[50x, 94.1x]

The histogram shows the distribution of RECOVERY Trial Cost Reduction Factor across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: RECOVERY Trial Cost Reduction Factor

This exceedance probability chart shows the likelihood that RECOVERY Trial Cost Reduction Factor will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

RECOVERY Trial Total QALYs Generated: 5.00M QALYs

📊 Details

Total QALYs generated by RECOVERY trial’s discoveries (lives saved × QALYs per life). Uses global impact methodology: counts all downstream health gains from the discovery.

Inputs:

RECOVERY Trial Global Lives Saved 📊: 1.00M lives (95% CI: 500k lives - 2.00M lives)
QALYs per COVID Death Averted: 5 QALYs/death (95% CI: 3 QALYs/death - 10 QALYs/death)

\[ \begin{gathered} QALY_{RECOVERY} \\ = Lives_{RECOVERY} \times QALY_{COVID} \\ = 1M \times 5 \\ = 5M \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for RECOVERY Trial Total QALYs Generated

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
QALYs Per Covid Death Averted	2.2404	Strong driver
Recovery Trial Global Lives Saved	-1.2571	Strong driver

Monte Carlo Distribution

Simulation Results Summary: RECOVERY Trial Total QALYs Generated

Statistic	Value
Baseline (deterministic)	5.00M
Mean (expected value)	5.57M
Median (50th percentile)	4.36M
Standard Deviation	4.03M
90% Confidence Interval	[1.51M, 14.3M]

The histogram shows the distribution of RECOVERY Trial Total QALYs Generated across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: RECOVERY Trial Total QALYs Generated

This exceedance probability chart shows the likelihood that RECOVERY Trial Total QALYs Generated will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Status Quo Average Years to First Treatment: 222 years

📊 Details

Average years until first treatment discovered for a typical disease under current system. At current discovery rates, the average disease waits half the total exploration time (~443/2 = ~222 years).

Inputs:

Status Quo Therapeutic Space Exploration Time 🔢: 443 years

\[ \begin{gathered} T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \end{gathered} \]

Methodology:¹³⁹

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Status Quo Average Years to First Treatment

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Status Quo Queue Clearance Years	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Status Quo Average Years to First Treatment

Statistic	Value
Baseline (deterministic)	222
Mean (expected value)	242
Median (50th percentile)	237
Standard Deviation	53.2
90% Confidence Interval	[162, 356]

The histogram shows the distribution of Status Quo Average Years to First Treatment across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Status Quo Average Years to First Treatment

This exceedance probability chart shows the likelihood that Status Quo Average Years to First Treatment will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Status Quo Therapeutic Space Exploration Time: 443 years

📊 Details

Years to explore the entire therapeutic search space under current system. At current discovery rate of ~15 diseases/year getting first treatments, finding treatments for all ~6,650 untreated diseases would take ~443 years.

Inputs:

Diseases Without Effective Treatment 🔢: 6.65k diseases
Diseases Getting First Treatment Per Year 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year)

\[ \begin{gathered} T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \end{gathered} \]

Methodology:¹³⁹

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Status Quo Therapeutic Space Exploration Time

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Diseases Without Effective Treatment	-0.7011	Strong driver
New Disease First Treatments Per Year	-0.2360	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Status Quo Therapeutic Space Exploration Time

Statistic	Value
Baseline (deterministic)	443
Mean (expected value)	485
Median (50th percentile)	475
Standard Deviation	106
90% Confidence Interval	[324, 712]

The histogram shows the distribution of Status Quo Therapeutic Space Exploration Time across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Status Quo Therapeutic Space Exploration Time

This exceedance probability chart shows the likelihood that Status Quo Therapeutic Space Exploration Time will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide DALYs Per Event: 41.8k DALYs

📊 Details

Total DALYs per US-scale thalidomide event (YLL + YLD)

Inputs:

Thalidomide YLD Per Event 🔢: 13.0k years
Thalidomide YLL Per Event 🔢: 28.8k years

\[ \begin{gathered} DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \\[0.5em] \text{where } YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \\[0.5em] \text{where } N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \\[0.5em] \text{where } YLL_{thal} = Deaths_{thal} \times 80 = 360 \times 80 = 28{,}800 \\[0.5em] \text{where } Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide DALYs Per Event

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide Yll Per Event	0.6300	Strong driver
Thalidomide Yld Per Event	0.3701	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide DALYs Per Event

Statistic	Value
Baseline (deterministic)	41.8k
Mean (expected value)	42.5k
Median (50th percentile)	40.8k
Standard Deviation	12.2k
90% Confidence Interval	[24.8k, 67.1k]

The histogram shows the distribution of Thalidomide DALYs Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide DALYs Per Event

This exceedance probability chart shows the likelihood that Thalidomide DALYs Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide Deaths Per Event: 360 deaths

📊 Details

Deaths per US-scale thalidomide event

Inputs:

Thalidomide Mortality Rate 📊: 40% (95% CI: 35% - 45%)
Thalidomide US Cases Prevented 🔢: 900 cases

\[ \begin{gathered} Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide Deaths Per Event

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide US Cases Prevented	1.5027	Strong driver
Thalidomide Mortality Rate	-0.5048	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide Deaths Per Event

Statistic	Value
Baseline (deterministic)	360
Mean (expected value)	364
Median (50th percentile)	353
Standard Deviation	95.8
90% Confidence Interval	[223, 556]

The histogram shows the distribution of Thalidomide Deaths Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide Deaths Per Event

This exceedance probability chart shows the likelihood that Thalidomide Deaths Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide Survivors Per Event: 540 cases

📊 Details

Survivors per US-scale thalidomide event

Inputs:

Thalidomide Mortality Rate 📊: 40% (95% CI: 35% - 45%)
Thalidomide US Cases Prevented 🔢: 900 cases

\[ \begin{gathered} N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide Survivors Per Event

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide Mortality Rate	0.5607	Strong driver
Thalidomide US Cases Prevented	0.4398	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide Survivors Per Event

Statistic	Value
Baseline (deterministic)	540
Mean (expected value)	537
Median (50th percentile)	531
Standard Deviation	86.3
90% Confidence Interval	[399, 698]

The histogram shows the distribution of Thalidomide Survivors Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide Survivors Per Event

This exceedance probability chart shows the likelihood that Thalidomide Survivors Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide US Cases Prevented: 900 cases

📊 Details

Estimated US thalidomide cases prevented by FDA rejection

Inputs:

Thalidomide Cases Worldwide 📊: 15.0k cases (95% CI: 10.0k cases - 20.0k cases)
US Population Share 1960 📊: 6% (95% CI: 5.5% - 6.5%)

\[ \begin{gathered} N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide US Cases Prevented

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide Cases Worldwide	1.3746	Strong driver
Thalidomide US Population Share 1960	-0.3756	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide US Cases Prevented

Statistic	Value
Baseline (deterministic)	900
Mean (expected value)	901
Median (50th percentile)	884
Standard Deviation	182
90% Confidence Interval	[622, 1.25k]

The histogram shows the distribution of Thalidomide US Cases Prevented across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide US Cases Prevented

This exceedance probability chart shows the likelihood that Thalidomide US Cases Prevented will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide YLD Per Event: 13.0k years

📊 Details

Years Lived with Disability per thalidomide event

Inputs:

Thalidomide Disability Weight 📊: 0.4:1 (95% CI: 0.32:1 - 0.48:1)
Thalidomide Survivors Per Event 🔢: 540 cases
Thalidomide Survivor Lifespan 📊: 60 years (95% CI: 50 years - 70 years)

\[ \begin{gathered} YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \\[0.5em] \text{where } N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide YLD Per Event

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide Disability Weight	28.4785	Strong driver
Thalidomide Survivor Lifespan	-23.4440	Strong driver
Thalidomide Survivors Per Event	-4.0444	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide YLD Per Event

Statistic	Value
Baseline (deterministic)	13.0k
Mean (expected value)	13.3k
Median (50th percentile)	12.6k
Standard Deviation	4.50k
90% Confidence Interval	[6.94k, 22.6k]

The histogram shows the distribution of Thalidomide YLD Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide YLD Per Event

This exceedance probability chart shows the likelihood that Thalidomide YLD Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide YLL Per Event: 28.8k years

📊 Details

Years of Life Lost per thalidomide event (infant deaths)

Inputs:

Thalidomide Deaths Per Event 🔢: 360 deaths

\[ \begin{gathered} YLL_{thal} = Deaths_{thal} \times 80 = 360 \times 80 = 28{,}800 \\[0.5em] \text{where } Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide YLL Per Event

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide Deaths Per Event	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide YLL Per Event

Statistic	Value
Baseline (deterministic)	28.8k
Mean (expected value)	29.2k
Median (50th percentile)	28.2k
Standard Deviation	7.67k
90% Confidence Interval	[17.9k, 44.5k]

The histogram shows the distribution of Thalidomide YLL Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide YLL Per Event

This exceedance probability chart shows the likelihood that Thalidomide YLL Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Annual Funding from 1% of Global Military Spending Redirected to DIH: $27.2B

📊 Details

Annual funding from 1% of global military spending redirected to DIH

Inputs:

Global Military Spending in 2024 📊: $2.72T
1% Reduction in Military Spending/War Costs from Treaty: 1%

\[ \begin{gathered} Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Treaty System Benefit Multiplier vs Childhood Vaccination Programs: 11.5x

📊 Details

Treaty system benefit multiplier vs childhood vaccination programs

Inputs:

Estimated Annual Global Economic Benefit from Childhood Vaccination Programs 📊: $15B (SE: ±$4.50B)
1% treaty Basic Annual Benefits (Peace + R&D Savings) 🔢: $172B

\[ \begin{gathered} k_{treaty:vax} = \frac{Benefit_{peace+RD}}{Benefit_{vax,ann}} = \frac{\$172B}{\$15B} = 11.5 \\[0.5em] \text{where } Benefit_{peace+RD} \\ = Benefit_{peace,soc} + Benefit_{RD,ann} \\ = \$114B + \$58.6B \\ = \$172B \\[0.5em] \text{where } Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \\[0.5em] \text{where } Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \\[0.5em] \text{where } Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \\[0.5em] \text{where } Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \\[0.5em] \text{where } Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \\[0.5em] \text{where } Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \\[0.5em] \text{where } Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \\[0.5em] \text{where } Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} \\ + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \\[0.5em] \text{where } Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \\[0.5em] \text{where } Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} \\ + Loss_{capital,conflict} + Cost_{psych} \\ + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \\[0.5em] \text{where } Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Treaty System Benefit Multiplier vs Childhood Vaccination Programs

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Childhood Vaccination Annual Benefit	-1.1963	Strong driver
Treaty Peace Plus R&D Annual Benefits	0.3259	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Treaty System Benefit Multiplier vs Childhood Vaccination Programs

Statistic	Value
Baseline (deterministic)	11.5x
Mean (expected value)	12.1x
Median (50th percentile)	11.8x
Standard Deviation	2.28x
90% Confidence Interval	[9x, 16.1x]

The histogram shows the distribution of Treaty System Benefit Multiplier vs Childhood Vaccination Programs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Treaty System Benefit Multiplier vs Childhood Vaccination Programs

This exceedance probability chart shows the likelihood that Treaty System Benefit Multiplier vs Childhood Vaccination Programs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Amortized Annual Treaty Campaign Cost: $250M

📊 Details

Amortized annual campaign cost (total cost ÷ campaign duration)

Inputs:

Treaty Campaign Duration: 4 years (95% CI: 3 years - 5 years)
Total 1% Treaty Campaign Cost 🔢: $1B

\[ \begin{gathered} Cost_{camp,amort} = \frac{Cost_{campaign}}{T_{campaign}} = \frac{\$1B}{4} = \$250M \\[0.5em] \text{where } Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Amortized Annual Treaty Campaign Cost

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Treaty Campaign Total Cost	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Amortized Annual Treaty Campaign Cost

Statistic	Value
Baseline (deterministic)	$250M
Mean (expected value)	$249M
Median (50th percentile)	$237M
Standard Deviation	$69.1M
90% Confidence Interval	[$158M, $379M]

The histogram shows the distribution of Amortized Annual Treaty Campaign Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Amortized Annual Treaty Campaign Cost

This exceedance probability chart shows the likelihood that Amortized Annual Treaty Campaign Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total 1% Treaty Campaign Cost: $1B

📊 Details

Total treaty campaign cost (100% VICTORY Incentive Alignment Bonds)

Inputs:

Viral Referendum Budget: $250M (95% CI: $150M - $410M)
Political Lobbying Campaign: Direct Lobbying, Super Pacs, Opposition Research, Staff, Legal/Compliance: $650M (95% CI: $325M - $1.30B)
Reserve Fund / Contingency Buffer: $100M (95% CI: $20M - $150M)

\[ \begin{gathered} Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total 1% Treaty Campaign Cost

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Treaty Campaign Budget Lobbying	0.9016	Strong driver
Treaty Campaign Budget Reserve	0.1026	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Total 1% Treaty Campaign Cost

Statistic	Value
Baseline (deterministic)	$1B
Mean (expected value)	$996M
Median (50th percentile)	$949M
Standard Deviation	$276M
90% Confidence Interval	[$632M, $1.51B]

The histogram shows the distribution of Total 1% Treaty Campaign Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total 1% Treaty Campaign Cost

This exceedance probability chart shows the likelihood that Total 1% Treaty Campaign Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Target Voting Bloc Size for Campaign: 280M of people

📊 Details

Target voting bloc size for campaign (3.5% of global population - critical mass for social change)

Inputs:

Global Population in 2024 📊: 8.00B of people (95% CI: 7.80B of people - 8.20B of people)
Critical Mass Threshold for Social Change 📊: 3.5% (95% CI: 2.5% - 4.5%)

\[ \begin{gathered} N_{voters,target} \\ = Pop_{global} \times Threshold_{activism} \\ = 8B \times 3.5\% \\ = 280M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Target Voting Bloc Size for Campaign

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Population Activism Threshold %	1.1097	Strong driver
Global Population 2024	-0.1099	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Target Voting Bloc Size for Campaign

Statistic	Value
Baseline (deterministic)	280M
Mean (expected value)	279M
Median (50th percentile)	276M
Standard Deviation	42.1M
90% Confidence Interval	[213M, 359M]

The histogram shows the distribution of Target Voting Bloc Size for Campaign across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Target Voting Bloc Size for Campaign

This exceedance probability chart shows the likelihood that Target Voting Bloc Size for Campaign will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput): $0.0018

📊 Details

Cost per DALY averted from elimination of efficacy lag plus earlier treatment discovery from increased trial throughput. Only counts campaign cost; ignores economic benefits from funding and R&D savings.

Inputs:

Total 1% Treaty Campaign Cost 🔢: $1B
Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput 🔢: 565B DALYs

\[ \begin{gathered} Cost_{treaty,DALY} = \frac{Cost_{campaign}}{DALYs_{max}} = \frac{\$1B}{565B} = \$0.00177 \\[0.5em] \text{where } Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Treaty Campaign Total Cost	0.6489	Strong driver
dFDA Trial Capacity Plus Efficacy Lag DALYs	-0.3320	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput)

Statistic	Value
Baseline (deterministic)	$0.0018
Mean (expected value)	$0.0019
Median (50th percentile)	$0.0016
Standard Deviation	$0.0011
90% Confidence Interval	[$0.0007, $0.0041]

The histogram shows the distribution of Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput)

This exceedance probability chart shows the likelihood that Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Expected Cost per DALY (Risk-Adjusted): $0.177

📊 Details

Expected cost per DALY accounting for political success probability uncertainty. Monte Carlo samples from beta(0.1%, 10%) distribution. At the conservative 1% estimate, this is still more cost-effective than bed nets ($89.0/DALY).

Inputs:

Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) 🔢: $0.0018
Political Success Probability 📊: 1% (95% CI: 0.1% - 10%)

\[ \begin{gathered} E[Cost_{DALY}] = \frac{Cost_{treaty,DALY}}{P_{success}} = \frac{\$0.00177}{1\%} = \$0.177 \\[0.5em] \text{where } Cost_{treaty,DALY} = \frac{Cost_{campaign}}{DALYs_{max}} = \frac{\$1B}{565B} = \$0.00177 \\[0.5em] \text{where } Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Expected Cost per DALY (Risk-Adjusted)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Treaty Cost Per DALY Trial Capacity Plus Efficacy Lag	0.5669	Strong driver
Political Success Probability	-0.4438	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Expected Cost per DALY (Risk-Adjusted)

Statistic	Value
Baseline (deterministic)	$0.177
Mean (expected value)	$1.06
Median (50th percentile)	$0.779
Standard Deviation	$1.12
90% Confidence Interval	[$0.029, $3.20]

The histogram shows the distribution of Expected Cost per DALY (Risk-Adjusted) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Expected Cost per DALY (Risk-Adjusted)

This exceedance probability chart shows the likelihood that Expected Cost per DALY (Risk-Adjusted) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Expected Treaty ROI (Risk-Adjusted): 848k:1

📊 Details

Expected ROI for 1% treaty accounting for political success probability uncertainty. Monte Carlo samples POLITICAL_SUCCESS_PROBABILITY from beta(0.1%, 10%) distribution to generate full expected value distribution. Central value uses 1% probability.

Inputs:

Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput 🔢: 84.8M:1
Political Success Probability 📊: 1% (95% CI: 0.1% - 10%)

\[ \begin{gathered} E[ROI_{max}] \\ = ROI_{max} \times P_{success} \\ = 84.8M \times 1\% \\ = 848{,}000 \\[0.5em] \text{where } ROI_{max} = \frac{Value_{max}}{Cost_{campaign}} = \frac{\$84800T}{\$1B} = 84.8M \\[0.5em] \text{where } Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \]

Methodology: Direct Calculation

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Expected Treaty ROI (Risk-Adjusted)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Political Success Probability	0.9453	Strong driver
Treaty ROI Trial Capacity Plus Efficacy Lag	0.1601	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Expected Treaty ROI (Risk-Adjusted)

Statistic	Value
Baseline (deterministic)	848k:1
Mean (expected value)	962k:1
Median (50th percentile)	154k:1
Standard Deviation	1.80M:1
90% Confidence Interval	[58.0k:1, 4.76M:1]

The histogram shows the distribution of Expected Treaty ROI (Risk-Adjusted) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Expected Treaty ROI (Risk-Adjusted)

This exceedance probability chart shows the likelihood that Expected Treaty ROI (Risk-Adjusted) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Expected Cost-Effectiveness vs Bed Nets Multiplier: 503x

📊 Details

Expected value multiplier vs bed nets (accounts for political uncertainty at 1% success rate)

Inputs:

Bed Nets Cost per DALY 📊: $89 (95% CI: $78 - $100)
Expected Cost per DALY (Risk-Adjusted) 🔢: $0.177

\[ \begin{gathered} E[k_{nets}] = \frac{Cost_{nets}}{E[Cost_{DALY}]} = \frac{\$89}{\$0.177} = 503 \\[0.5em] \text{where } E[Cost_{DALY}] = \frac{Cost_{treaty,DALY}}{P_{success}} = \frac{\$0.00177}{1\%} = \$0.177 \\[0.5em] \text{where } Cost_{treaty,DALY} = \frac{Cost_{campaign}}{DALYs_{max}} = \frac{\$1B}{565B} = \$0.00177 \\[0.5em] \text{where } Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Expected Cost-Effectiveness vs Bed Nets Multiplier

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Treaty Expected Cost Per DALY	-0.4156	Moderate driver
Bed Nets Cost Per DALY	0.0039	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Expected Cost-Effectiveness vs Bed Nets Multiplier

Statistic	Value
Baseline (deterministic)	503x
Mean (expected value)	605x
Median (50th percentile)	109x
Standard Deviation	1.2kx
90% Confidence Interval	[29.9x, 3.0kx]

The histogram shows the distribution of Expected Cost-Effectiveness vs Bed Nets Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Expected Cost-Effectiveness vs Bed Nets Multiplier

This exceedance probability chart shows the likelihood that Expected Cost-Effectiveness vs Bed Nets Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

1% treaty Basic Annual Benefits (Peace + R&D Savings): $172B

📊 Details

Basic annual benefits: peace dividend + Decentralized Framework for Drug Assessment R&D savings only (2 of 8 benefit categories, excludes regulatory delay value)

Inputs:

Annual Peace Dividend from 1% Reduction in Total War Costs 🔢: $114B
Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $58.6B

\[ \begin{gathered} Benefit_{peace+RD} \\ = Benefit_{peace,soc} + Benefit_{RD,ann} \\ = \$114B + \$58.6B \\ = \$172B \\[0.5em] \text{where } Benefit_{peace,soc} \\ = Cost_{war,total} \times Reduce_{treaty} \\ = \$11.4T \times 1\% \\ = \$114B \\[0.5em] \text{where } Cost_{war,total} \\ = Cost_{war,direct} + Cost_{war,indirect} \\ = \$7.66T + \$3.7T \\ = \$11.4T \\[0.5em] \text{where } Cost_{war,direct} \\ = Loss_{life,conflict} + Damage_{infra,total} \\ + Disruption_{trade} + Spending_{mil} \\ = \$2.45T + \$1.88T + \$616B + \$2.72T \\ = \$7.66T \\[0.5em] \text{where } Loss_{life,conflict} \\ = Cost_{combat,human} + Cost_{state,human} \\ + Cost_{terror,human} \\ = \$2.34T + \$27B + \$83B \\ = \$2.45T \\[0.5em] \text{where } Cost_{combat,human} \\ = Deaths_{combat} \times VSL \\ = 234{,}000 \times \$10M \\ = \$2.34T \\[0.5em] \text{where } Cost_{state,human} \\ = Deaths_{state} \times VSL \\ = 2{,}700 \times \$10M \\ = \$27B \\[0.5em] \text{where } Cost_{terror,human} \\ = Deaths_{terror} \times VSL \\ = 8{,}300 \times \$10M \\ = \$83B \\[0.5em] \text{where } Damage_{infra,total} \\ = Damage_{comms} + Damage_{edu} + Damage_{energy} \\ + Damage_{health} + Damage_{transport} \\ + Damage_{water} \\ = \$298B + \$234B + \$422B + \$166B + \$487B + \$268B \\ = \$1.88T \\[0.5em] \text{where } Disruption_{trade} \\ = Disruption_{currency} + Disruption_{energy} \\ + Disruption_{shipping} + Disruption_{supply} \\ = \$57.4B + \$125B + \$247B + \$187B \\ = \$616B \\[0.5em] \text{where } Cost_{war,indirect} \\ = Damage_{env} + Loss_{growth,mil} \\ + Loss_{capital,conflict} + Cost_{psych} \\ + Cost_{refugee} + Cost_{vet} \\ = \$100B + \$2.72T + \$300B + \$232B + \$150B + \$200B \\ = \$3.7T \\[0.5em] \text{where } Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for 1% treaty Basic Annual Benefits (Peace + R&D Savings)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Peace Dividend Annual Societal Benefit	0.6828	Strong driver
dFDA Benefit R&D Only Annual	0.3457	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: 1% treaty Basic Annual Benefits (Peace + R&D Savings)

Statistic	Value
Baseline (deterministic)	$172B
Mean (expected value)	$172B
Median (50th percentile)	$170B
Standard Deviation	$22.2B
90% Confidence Interval	[$140B, $213B]

The histogram shows the distribution of 1% treaty Basic Annual Benefits (Peace + R&D Savings) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: 1% treaty Basic Annual Benefits (Peace + R&D Savings)

This exceedance probability chart shows the likelihood that 1% treaty Basic Annual Benefits (Peace + R&D Savings) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput: 84.8M:1

📊 Details

Treaty ROI from elimination of efficacy lag plus earlier treatment discovery from increased trial throughput. Total one-time benefit divided by campaign cost. This is the primary ROI estimate for total health benefits.

Inputs:

Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput 🔢: $84.8 quadrillion
Total 1% Treaty Campaign Cost 🔢: $1B

\[ \begin{gathered} ROI_{max} = \frac{Value_{max}}{Cost_{campaign}} = \frac{\$84800T}{\$1B} = 84.8M \\[0.5em] \text{where } Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Treaty Campaign Total Cost	-0.7928	Strong driver
dFDA Trial Capacity Plus Efficacy Lag Economic Value	0.3363	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput

Statistic	Value
Baseline (deterministic)	84.8M:1
Mean (expected value)	95.1M:1
Median (50th percentile)	96.0M:1
Standard Deviation	28.2M:1
90% Confidence Interval	[46.6M:1, 144M:1]

The histogram shows the distribution of Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput

This exceedance probability chart shows the likelihood that Treaty ROI - Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Treaty System Costs: $290M

📊 Details

Total annual system costs (campaign + Decentralized Framework for Drug Assessment operations)

Inputs:

Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M
Amortized Annual Treaty Campaign Cost 🔢: $250M

\[ \begin{gathered} Cost_{treaty,ann} \\ = OPEX_{dFDA} + Cost_{camp,amort} \\ = \$40M + \$250M \\ = \$290M \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Cost_{camp,amort} = \frac{Cost_{campaign}}{T_{campaign}} = \frac{\$1B}{4} = \$250M \\[0.5em] \text{where } Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Treaty System Costs

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Treaty Campaign Annual Cost Amortized	0.8951	Strong driver
dFDA Annual OPEX	0.1063	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Total Annual Treaty System Costs

Statistic	Value
Baseline (deterministic)	$290M
Mean (expected value)	$289M
Median (50th percentile)	$276M
Standard Deviation	$77.2M
90% Confidence Interval	[$185M, $434M]

The histogram shows the distribution of Total Annual Treaty System Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Treaty System Costs

This exceedance probability chart shows the likelihood that Total Annual Treaty System Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Cost-Effectiveness vs Bed Nets Multiplier: 50.3kx

📊 Details

How many times more cost-effective than bed nets (using $89/DALY midpoint estimate)

Inputs:

Bed Nets Cost per DALY 📊: $89 (95% CI: $78 - $100)
Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) 🔢: $0.0018

\[ \begin{gathered} k_{treaty:nets} \\ = \frac{Cost_{nets}}{Cost_{treaty,DALY}} \\ = \frac{\$89}{\$0.00177} \\ = 50{,}300 \\[0.5em] \text{where } Cost_{treaty,DALY} = \frac{Cost_{campaign}}{DALYs_{max}} = \frac{\$1B}{565B} = \$0.00177 \\[0.5em] \text{where } Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Cost-Effectiveness vs Bed Nets Multiplier

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Bed Nets Cost Per DALY	-0.8686	Strong driver
Treaty Cost Per DALY Trial Capacity Plus Efficacy Lag	-0.0847	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Cost-Effectiveness vs Bed Nets Multiplier

Statistic	Value
Baseline (deterministic)	50.3kx
Mean (expected value)	59.9kx
Median (50th percentile)	56.9kx
Standard Deviation	25.0kx
90% Confidence Interval	[23.7kx, 111.7kx]

The histogram shows the distribution of Cost-Effectiveness vs Bed Nets Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Cost-Effectiveness vs Bed Nets Multiplier

This exceedance probability chart shows the likelihood that Cost-Effectiveness vs Bed Nets Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Treaty Campaign Leverage vs Direct Funding: 475x

📊 Details

How many times more cost-effective the treaty campaign is vs direct funding. Treaty achieves 542× leverage: $1B campaign unlocks $27.2B/year government funding for 46.5 years (exploration period, NPV: $541.9B), avoiding need for philanthropists/NIH to directly commit this amount. Both approaches achieve same 200B DALY timeline shift benefit by exploring the therapeutic space 9.5× faster. Treaty spreads cost across governments while building sustainable public funding infrastructure.

Inputs:

dFDA Direct Funding Cost per DALY 🔢: $0.841
Cost per DALY Averted (Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Increased Trial Throughput) 🔢: $0.0018

\[ \begin{gathered} Leverage_{treaty} \\ = \frac{Cost_{direct,DALY}}{Cost_{treaty,DALY}} \\ = \frac{\$0.841}{\$0.00177} \\ = 475 \\[0.5em] \text{where } Cost_{direct,DALY} = \frac{NPV_{direct}}{DALYs_{max}} = \frac{\$475B}{565B} = \$0.841 \\[0.5em] \text{where } NPV_{direct} \\ = \frac{T_{queue,dFDA}}{Treasury_{RD,ann} \times r_{discount}} \\ = \frac{36}{\$21.8B \times 3\%} \\ = \$475B \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } T_{queue,dFDA} = \frac{T_{queue,SQ}}{k_{capacity}} = \frac{443}{12.3} = 36 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Cost_{treaty,DALY} = \frac{Cost_{campaign}}{DALYs_{max}} = \frac{\$1B}{565B} = \$0.00177 \\[0.5em] \text{where } Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Treaty Campaign Leverage vs Direct Funding

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Direct Funding Cost Per DALY	4.1716	Strong driver
Treaty Cost Per DALY Trial Capacity Plus Efficacy Lag	-3.7769	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Treaty Campaign Leverage vs Direct Funding

Statistic	Value
Baseline (deterministic)	475x
Mean (expected value)	421x
Median (50th percentile)	438x
Standard Deviation	47.4x
90% Confidence Interval	[329x, 462x]

The histogram shows the distribution of Treaty Campaign Leverage vs Direct Funding across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Treaty Campaign Leverage vs Direct Funding

This exceedance probability chart shows the likelihood that Treaty Campaign Leverage vs Direct Funding will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Cumulative Trial Capacity Years Over 20 Years: 246 years

📊 Details

Cumulative trial-capacity-equivalent years over 20-year period

Inputs:

Trial Capacity Multiplier 🔢: 12.3x

\[ \begin{gathered} Capacity_{20yr} = k_{capacity} \times 20 = 12.3 \times 20 = 246 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Cumulative Trial Capacity Years Over 20 Years

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Multiplier	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Cumulative Trial Capacity Years Over 20 Years

Statistic	Value
Baseline (deterministic)	246
Mean (expected value)	441
Median (50th percentile)	320
Standard Deviation	404
90% Confidence Interval	[83.8, 1.23k]

The histogram shows the distribution of Cumulative Trial Capacity Years Over 20 Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Cumulative Trial Capacity Years Over 20 Years

This exceedance probability chart shows the likelihood that Cumulative Trial Capacity Years Over 20 Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Ratio of Type Ii Error Cost to Type I Error Benefit: 3.07k:1

📊 Details

Ratio of Type II error cost to Type I error benefit (harm from delay vs. harm prevented)

Inputs:

Total DALYs Lost from Disease Eradication Delay 🔢: 7.94B DALYs
Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) 🔢: 2.59M DALYs

\[ \begin{gathered} Ratio_{TypeII} = \frac{DALYs_{lag}}{DALY_{TypeI}} = \frac{7.94B}{2.59M} = 3{,}070 \\[0.5em] \text{where } DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.07B + 873M = 7.94B \\[0.5em] \text{where } YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } DALY_{TypeI} = DALY_{thal} \times 62 = 41{,}800 \times 62 = 2.59M \\[0.5em] \text{where } DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \\[0.5em] \text{where } YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \\[0.5em] \text{where } N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \\[0.5em] \text{where } YLL_{thal} = Deaths_{thal} \times 80 = 360 \times 80 = 28{,}800 \\[0.5em] \text{where } Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Ratio of Type Ii Error Cost to Type I Error Benefit

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Efficacy Lag Elimination DALYs	7.2872	Strong driver
Type I Error Benefit DALYs	-7.1207	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Ratio of Type Ii Error Cost to Type I Error Benefit

Statistic	Value
Baseline (deterministic)	3.07k:1
Mean (expected value)	3.05k:1
Median (50th percentile)	3.09k:1
Standard Deviation	101:1
90% Confidence Interval	[2.88k:1, 3.12k:1]

The histogram shows the distribution of Ratio of Type Ii Error Cost to Type I Error Benefit across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Ratio of Type Ii Error Cost to Type I Error Benefit

This exceedance probability chart shows the likelihood that Ratio of Type Ii Error Cost to Type I Error Benefit will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024): 2.59M DALYs

📊 Details

Maximum DALYs saved by FDA preventing unsafe drugs over 62-year period 1962-2024 (extreme overestimate: one Thalidomide-scale event per year)

Inputs:

Thalidomide DALYs Per Event 🔢: 41.8k DALYs

\[ \begin{gathered} DALY_{TypeI} = DALY_{thal} \times 62 = 41{,}800 \times 62 = 2.59M \\[0.5em] \text{where } DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \\[0.5em] \text{where } YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \\[0.5em] \text{where } N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \\[0.5em] \text{where } YLL_{thal} = Deaths_{thal} \times 80 = 360 \times 80 = 28{,}800 \\[0.5em] \text{where } Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide DALYs Per Event	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)

Statistic	Value
Baseline (deterministic)	2.59M
Mean (expected value)	2.63M
Median (50th percentile)	2.53M
Standard Deviation	754k
90% Confidence Interval	[1.54M, 4.16M]

The histogram shows the distribution of Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)

This exceedance probability chart shows the likelihood that Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Unexplored Therapeutic Frontier: 99.7%

📊 Details

Fraction of possible drug-disease space that remains unexplored (>99%)

Inputs:

Tested Drug-Disease Relationships: 32.5k relationships (95% CI: 15.0k relationships - 50.0k relationships)
Possible Drug-Disease Combinations 🔢: 9.50M combinations

\[ \begin{gathered} Ratio_{unexplored} \\ = 1 - \frac{N_{tested}}{N_{combos}} \\ = 1 - \frac{32{,}500}{9.5M} \\ = 99.7\% \\[0.5em] \text{where } N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Unexplored Therapeutic Frontier

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Tested Relationships Estimate	-1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Unexplored Therapeutic Frontier

Statistic	Value
Baseline (deterministic)	99.7%
Mean (expected value)	99.7%
Median (50th percentile)	99.7%
Standard Deviation	0.0868%
90% Confidence Interval	[99.5%, 99.8%]

The histogram shows the distribution of Unexplored Therapeutic Frontier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Unexplored Therapeutic Frontier

This exceedance probability chart shows the likelihood that Unexplored Therapeutic Frontier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Annual VICTORY Incentive Alignment Bond Payout: $2.72B

📊 Details

Annual VICTORY Incentive Alignment Bond payout (treaty funding × bond percentage)

Inputs:

Annual Funding from 1% of Global Military Spending Redirected to DIH 🔢: $27.2B
Percentage of Captured Dividend Funding VICTORY Incentive Alignment Bonds: 10%

\[ \begin{gathered} Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Annual Return Percentage for VICTORY Incentive Alignment Bondholders: 272%

📊 Details

Annual return percentage for VICTORY Incentive Alignment Bondholders

Inputs:

Annual VICTORY Incentive Alignment Bond Payout 🔢: $2.72B
Total 1% Treaty Campaign Cost 🔢: $1B

\[ \begin{gathered} r_{bond} = \frac{Payout_{bond,ann}}{Cost_{campaign}} = \frac{\$2.72B}{\$1B} = 272\% \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Cost_{campaign} \\ = Budget_{viral,base} + Budget_{lobby,treaty} \\ + Budget_{reserve} \\ = \$250M + \$650M + \$100M \\ = \$1B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Annual Return Percentage for VICTORY Incentive Alignment Bondholders

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Treaty Campaign Total Cost	-0.9366	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Annual Return Percentage for VICTORY Incentive Alignment Bondholders

Statistic	Value
Baseline (deterministic)	272%
Mean (expected value)	293%
Median (50th percentile)	287%
Standard Deviation	76.3%
90% Confidence Interval	[180%, 430%]

The histogram shows the distribution of Annual Return Percentage for VICTORY Incentive Alignment Bondholders across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Annual Return Percentage for VICTORY Incentive Alignment Bondholders

This exceedance probability chart shows the likelihood that Annual Return Percentage for VICTORY Incentive Alignment Bondholders will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

External Data Sources

Parameters sourced from peer-reviewed publications, institutional databases, and authoritative reports.

ADAPTABLE Trial Cost per Patient: $929

📊 Details

Cost per patient in ADAPTABLE trial ($14M PCORI grant / 15,076 patients). Note: This is the direct grant cost; true cost including in-kind may be 10-40% higher.

Source:¹

Uncertainty Range

Technical: 95% CI: [$929, $1.40K] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $929 and $1.40K (±25%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: ADAPTABLE Trial Cost per Patient

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

ADAPTABLE Trial Total Cost: $14M

📊 Details

PCORI grant for ADAPTABLE trial (2016-2019). Note: Direct funding only; total costs including site overhead and in-kind contributions from health systems may be higher.

Source:¹

Uncertainty Range

Technical: 95% CI: [$14M, $20M] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between $14M and $20M (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: ADAPTABLE Trial Total Cost

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

Antidepressant Trial Exclusion Rate: 86.1%

📊 Details

Mean exclusion rate in antidepressant trials (86.1% of real-world patients excluded)

Source:²

✓ High confidence

Average Annual Stock Market Return: 10%

📊 Details

Average annual stock market return (10%)

Source:³

✓ High confidence

Bed Nets Cost per DALY: $89

📊 Details

GiveWell cost per DALY for insecticide-treated bed nets (midpoint estimate, range $78-100). DALYs (Disability-Adjusted Life Years) measure disease burden by combining years of life lost and years lived with disability. Bed nets prevent malaria deaths and are considered a gold standard benchmark for cost-effective global health interventions - if an intervention costs less per DALY than bed nets, it’s exceptionally cost-effective. GiveWell synthesizes peer-reviewed academic research with transparent, rigorous methodology and extensive external expert review.

Source:¹⁸

Uncertainty Range

Technical: 95% CI: [$78, $100] • Distribution: Normal

What this means: This estimate has moderate uncertainty. The true value likely falls between $78 and $100 (±12%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The normal distribution means values cluster around the center with equal chances of being higher or lower.

Input Distribution

Probability Distribution: Bed Nets Cost per DALY

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed

Estimated Annual Global Economic Benefit from Childhood Vaccination Programs: $15B

📊 Details

Estimated annual global economic benefit from childhood vaccination programs (measles, polio, etc.)

Source:²⁰

Uncertainty Range

Technical: Distribution: Lognormal (SE: $4.50B)

Input Distribution

Probability Distribution: Estimated Annual Global Economic Benefit from Childhood Vaccination Programs

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Return on Investment from Childhood Vaccination Programs: 13:1

📊 Details

Return on investment from childhood vaccination programs

Source:²¹

✓ High confidence

Disability Weight for Untreated Chronic Conditions: 0.35 weight

📊 Details

Disability weight for untreated chronic conditions (WHO Global Burden of Disease)

Source:¹⁷

Uncertainty Range

Technical: Distribution: Normal (SE: 0.07 weight)

Input Distribution

Probability Distribution: Disability Weight for Untreated Chronic Conditions

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence • 📊 Peer-reviewed

Current Active Trials at Any Given Time: 10.0k trials

📊 Details

Current active trials at any given time (3-5 year duration)

Source:²⁴

✓ High confidence

Current Clinical Trial Participation Rate: 0.06%

📊 Details

Current clinical trial participation rate (0.06% of population)

Source:²⁵

✓ High confidence

Global Population with Chronic Diseases: 2.40B people

📊 Details

Global population with chronic diseases

Source:²⁶

Uncertainty Range

Technical: 95% CI: [2.00B people, 2.80B people] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between 2.00B people and 2.80B people (±17%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Global Population with Chronic Diseases

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Current Global Clinical Trials per Year: 3.30k trials/year

📊 Details

Current global clinical trials per year

Source:³⁰

Uncertainty Range

Technical: 95% CI: [2.64k trials/year, 3.96k trials/year] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between 2.64k trials/year and 3.96k trials/year (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Current Global Clinical Trials per Year

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Current Trial Abandonment Rate: 40%

📊 Details

Current trial abandonment rate (40% never complete)

Source:²⁸

✓ High confidence

Annual Global Clinical Trial Participants: 1.90M patients/year

📊 Details

Annual global clinical trial participants (IQVIA 2022: 1.9M post-COVID normalization)

Source:²⁹

Uncertainty Range

Technical: 95% CI: [1.50M patients/year, 2.30M patients/year] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between 1.50M patients/year and 2.30M patients/year (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Annual Global Clinical Trial Participants

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Annual Defense Industry Lobbying Spending: $127M

📊 Details

Annual defense industry lobbying spending

Source:³¹

✓ High confidence • 📊 Peer-reviewed • Updated 2024

Deworming Cost per DALY: $55

📊 Details

Cost per DALY for deworming programs (range $28-82, midpoint estimate). GiveWell notes this 2011 estimate is outdated and their current methodology focuses on long-term income effects rather than short-term health DALYs.

Source:³²

? Low confidence

dFDA Pragmatic Trial Cost per Patient: $929

📊 Details

dFDA pragmatic trial cost per patient. Uses ADAPTABLE trial ($929) as DELIBERATELY CONSERVATIVE central estimate. Harvard meta-analysis of 108 trials found median of only $97/patient - our estimate may overstate costs by 10x. Confidence interval spans meta-analysis median to complex chronic disease trials.

Source:¹

Uncertainty Range

Technical: 95% CI: [$97, $3K] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between $97 and $3K (±156%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: dFDA Pragmatic Trial Cost per Patient

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

Drug Development Cost (1980s): $194M

📊 Details

Drug development cost in 1980s (compounded to approval, 1990 dollars)

Source:³³

Uncertainty Range

Technical: 95% CI: [$146M, $242M] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between $146M and $242M (±25%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Drug Development Cost (1980s)

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Drug Discovery to Approval Timeline: 14 years

📊 Details

Full drug development timeline from discovery to FDA approval. Typical range is 12-15 years based on BIO 2021 and PMC meta-analyses. Breakdown: preclinical 4-6 years + clinical 10.5 years. Using 14 years as central estimate.

Source:³⁴

Uncertainty Range

Technical: 95% CI: [12 years, 17 years]

What this means: This estimate has moderate uncertainty. The true value likely falls between 12 years and 17 years (±18%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

Input Distribution

Probability Distribution: Drug Discovery to Approval Timeline

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Drug Repurposing Success Rate: 30%

📊 Details

Percentage of drugs that gain at least one new indication after initial approval

Source:³⁵

✓ High confidence

Economic Multiplier for Healthcare Investment: 4.3x

📊 Details

Economic multiplier for healthcare investment (4.3x ROI). Literature range 3.0-6.0×.

Source:³⁷

Uncertainty Range

Technical: 95% CI: [3x, 6x] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between 3x and 6x (±35%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Economic Multiplier for Healthcare Investment

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Economic Multiplier for Military Spending: 0.6x

📊 Details

Economic multiplier for military spending (0.6x ROI). Literature range 0.4-1.0×.

Source:³⁹

Uncertainty Range

Technical: 95% CI: [0.4x, 0.9x] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between 0.4x and 0.9x (±42%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Economic Multiplier for Military Spending

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Regulatory Delay for Efficacy Testing Post-Safety Verification: 8.2 years

📊 Details

Regulatory delay for efficacy testing (Phase II/III) post-safety verification. Based on BIO 2021 industry survey. Note: This is for drugs that COMPLETE the pipeline - survivor bias means actual delay for any given disease may be longer if candidates fail and must restart.

Source:³⁴

Uncertainty Range

Technical: Distribution: Normal (SE: 2 years)

Input Distribution

Probability Distribution: Regulatory Delay for Efficacy Testing Post-Safety Verification

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence • 📊 Peer-reviewed • Updated 2021

Givewell Cost per Life Saved (Maximum): $5.50K

📊 Details

GiveWell cost per life saved (Against Malaria Foundation)

Source:¹⁸

✓ High confidence

Givewell Cost per Life Saved (Minimum): $3.50K

📊 Details

Source:¹⁷

Uncertainty Range

Probability Distribution: Global Life Expectancy (2024)

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed • Updated 2024

Global Government Medical Research Spending: $67.5B

📊 Details

Global government medical research spending

Source:⁵⁹

Uncertainty Range

Technical: 95% CI: [$54B, $81B] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between $54B and $81B (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Global Government Medical Research Spending

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Global Military Spending in 2024: $2.72T

📊 Details

Global military spending in 2024

Source:⁶⁰

Uncertainty Range

Proportion of global DALYs that are YLD (years lived with disability) vs YLL (years of life lost). From GBD 2021: 1.13B YLD out of 2.88B total DALYs = 39%.

Source:⁴⁵

Uncertainty Range

Technical: Distribution: Normal (SE: 0.03 proportion)

Input Distribution

Probability Distribution: YLD Proportion of Total DALYs

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed

Human Interactome Targeted by Drugs: 12%

📊 Details

Probability Distribution: Pharma Drug Development Cost (Current System)

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed

Phase I Safety Trial Duration: 2.3 years

📊 Details

Phase I safety trial duration

Source:³⁴

✓ High confidence • 📊 Peer-reviewed • Updated 2021

Pragmatic Trial Median Cost per Patient (PMC Review): $97

📊 Details

Median cost per patient in embedded pragmatic clinical trials (systematic review of 64 trials). IQR: $19-$478 (2015 USD).

Source:⁸⁵

Uncertainty Range

Technical: 95% CI: [$19, $478] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between $19 and $478 (±237%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Pragmatic Trial Median Cost per Patient (PMC Review)

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Political Success Probability: 1%

📊 Details

Estimated probability of treaty ratification and sustained implementation. Central estimate 1% is conservative. This assumes 99% chance of failure.

Source:⁹⁰

Uncertainty Range

Technical: 95% CI: [0.1%, 10%] • Distribution: Beta (SE: 2%)

What this means: This estimate is highly uncertain. The true value likely falls between 0.1% and 10% (±495%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The beta distribution means values are bounded and can skew toward one end.

Input Distribution

Probability Distribution: Political Success Probability

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

? Low confidence

Percentage Military Spending Cut After WW2: 30%

📊 Details

Percentage military spending cut after WW2 (historical precedent)

Source:⁹³

✓ High confidence

Pre-1962 Drug Development Cost (2024 Dollars): $24.7M

📊 Details

Pre-1962 drug development cost adjusted to 2024 dollars ($6.5M × 3.80 = $24.7M, CPI-adjusted from Baily 1972)

Source:⁹⁴

Uncertainty Range

Technical: 95% CI: [$19.5M, $30M] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between $19.5M and $30M (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Pre-1962 Drug Development Cost (2024 Dollars)

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed

Pre-1962 Physician Count (Unverified): 144k physicians

📊 Details

Probability Distribution: RECOVERY Trial Total Cost

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Mean Age of Preventable Death from Post-Safety Efficacy Delay: 62 years

📊 Details

Mean age of preventable death from post-safety efficacy testing regulatory delay (Phase 2-4)

Source:¹⁷

Uncertainty Range

Technical: Distribution: Normal (SE: 3 years)

Input Distribution

Probability Distribution: Mean Age of Preventable Death from Post-Safety Efficacy Delay

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence • 📊 Peer-reviewed

Pre-Death Suffering Period During Post-Safety Efficacy Delay: 6 years

📊 Details

Pre-death suffering period during post-safety efficacy testing delay (average years lived with untreated condition while awaiting Phase 2-4 completion)

Source:¹⁷

Uncertainty Range

Technical: 95% CI: [4 years, 9 years] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between 4 years and 9 years (±42%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Pre-Death Suffering Period During Post-Safety Efficacy Delay

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence • 📊 Peer-reviewed

Return on Investment from Smallpox Eradication Campaign: 280:1

📊 Details

Return on investment from smallpox eradication campaign

Source:¹⁰³

✓ High confidence

Standard Economic Value per QALY: $150K

📊 Details

Standard economic value per QALY

Source:¹⁰⁵

Uncertainty Range

Technical: Distribution: Normal (SE: $30K)

Input Distribution

Probability Distribution: Standard Economic Value per QALY

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Deaths from 9/11 Terrorist Attacks: 3.00k deaths

📊 Details

Deaths from 9/11 terrorist attacks

Source:¹¹¹

Probability Distribution: Value of Statistical Life

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Vitamin A Supplementation Cost per DALY: $37

📊 Details

Cost per DALY for vitamin A supplementation programs (India: $23-50; Africa: $40-255; wide variation by region and baseline VAD prevalence). Using India midpoint as conservative estimate.

Source:¹³⁴

~ Medium confidence

Return on Investment from Water Fluoridation Programs: 23:1

📊 Details

Return on investment from water fluoridation programs

Source:¹³⁵

✓ High confidence

Cost-Effectiveness Threshold ($50,000/QALY): $50K

📊 Details

Cost-effectiveness threshold widely used in US health economics ($50,000/QALY, from 1980s dialysis costs)

Source:¹³⁶

✓ High confidence

Core Definitions

Fundamental parameters and constants used throughout the analysis.

ADAPTABLE Trial Patients Enrolled: 15.1k patients

📊 Details

📊 Details

Percentage of deaths that are eventually avoidable with sufficient biomedical research and technological advancement. Central estimate ~92% based on ~7.9% fundamentally unavoidable (primarily accidents). Wide uncertainty reflects debate over: (1) aging as addressable vs. fundamental, (2) asymptotic difficulty of last diseases, (3) multifactorial disease complexity.

Uncertainty Range

Technical: 95% CI: [50%, 98%] • Distribution: Beta

The beta distribution means values are bounded and can skew toward one end.

Input Distribution

Probability Distribution: Eventually Avoidable Death Percentage

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

Core definition

Fundamentally Unavoidable Death Percentage: 7.37%

📊 Details

📊 Details

Viral referendum budget for 280M verified votes (base: $250M realistic with $0.50/vote avg, range: $150M optimistic $0.20/vote to $410M worst-case $1.05/vote). Components: platform ($35M), verification infrastructure (280M × friction × $0.18-0.20), tiered referral payments (varies by virality and marginal cost curve per diffusion theory), marketing seed ($5-15M). Based on PayPal referral economics ($18-36 inflation-adjusted) and biometric verification pricing ($0.15-0.25 at 300M+ scale).

Uncertainty Range

Technical: 95% CI: [$150M, $410M]

What this means: This estimate is highly uncertain. The true value likely falls between $150M and $410M (±52%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

Input Distribution

Probability Distribution: Viral Referendum Budget

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

Core definition

1% Reduction in Military Spending/War Costs from Treaty: 1%

📊 Details

1% reduction in military spending/war costs from treaty

Uncertainty Range

Technical: Distribution: Fixed

Core definition

Trial-Relevant Diseases: 1.00k diseases

📊 Details

Consolidated count of trial-relevant diseases worth targeting (after grouping ICD-10 codes)

Uncertainty Range

Technical: 95% CI: [800 diseases, 1.20k diseases] • Distribution: Uniform

What this means: This estimate has moderate uncertainty. The true value likely falls between 800 diseases and 1.20k diseases (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The uniform distribution means any value in the range is equally likely.

Input Distribution

Probability Distribution: Trial-Relevant Diseases

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

Core definition

Percentage of Captured Dividend Funding VICTORY Incentive Alignment Bonds: 10%

📊 Details

Percentage of captured dividend funding VICTORY Incentive Alignment Bonds (10%)

Uncertainty Range

Technical: Distribution: Fixed

Core definition

Fund, N. C. NIH pragmatic trials: Minimal funding despite 30x cost advantage. NIH Common Fund: HCS Research Collaboratory https://commonfund.nih.gov/hcscollaboratory (2025)

The NIH Pragmatic Trials Collaboratory funds trials at **$500K for planning phase, $1M/year for implementation**—a tiny fraction of NIH’s budget. The ADAPTABLE trial cost **$14 million** for **15,076 patients** (= **$929/patient**) versus **$420 million** for a similar traditional RCT (30x cheaper), yet pragmatic trials remain severely underfunded. PCORnet infrastructure enables real-world trials embedded in healthcare systems, but receives minimal support compared to basic research funding. Additional sources: https://commonfund.nih.gov/hcscollaboratory | https://pcornet.org/wp-content/uploads/2025/08/ADAPTABLE_Lay_Summary_21JUL2025.pdf | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604499/

NIH. Antidepressant clinical trial exclusion rates. Zimmerman et al. https://pubmed.ncbi.nlm.nih.gov/26276679/ (2015)

Mean exclusion rate: 86.1% across 158 antidepressant efficacy trials (range: 44.4% to 99.8%) More than 82% of real-world depression patients would be ineligible for antidepressant registration trials Exclusion rates increased over time: 91.4% (2010-2014) vs. 83.8% (1995-2009) Most common exclusions: comorbid psychiatric disorders, age restrictions, insufficient depression severity, medical conditions Emergency psychiatry patients: only 3.3% eligible (96.7% excluded) when applying 9 common exclusion criteria Only a minority of depressed patients seen in clinical practice are likely to be eligible for most AETs Note: Generalizability of antidepressant trials has decreased over time, with increasingly stringent exclusion criteria eliminating patients who would actually use the drugs in clinical practice Additional sources: https://pubmed.ncbi.nlm.nih.gov/26276679/ | https://pubmed.ncbi.nlm.nih.gov/26164052/ | https://www.wolterskluwer.com/en/news/antidepressant-trials-exclude-most-real-world-patients-with-depression

CNBC. Warren buffett’s career average investment return. CNBC https://www.cnbc.com/2025/05/05/warren-buffetts-return-tally-after-60-years-5502284percent.html (2025)

Berkshire’s compounded annual return from 1965 through 2024 was 19.9%, nearly double the 10.4% recorded by the S&P 500. Berkshire shares skyrocketed 5,502,284% compared to the S&P 500’s 39,054% rise during that period. Additional sources: https://www.cnbc.com/2025/05/05/warren-buffetts-return-tally-after-60-years-5502284percent.html | https://www.slickcharts.com/berkshire-hathaway/returns

Group, E. W. US farm subsidy database and analysis. Environmental Working Group https://farm.ewg.org/ (2024)

US agricultural subsidies total approximately $30 billion annually, but create much larger economic distortions. Top 10% of farms receive 78% of subsidies, benefits concentrated in commodity crops (corn, soy, wheat, cotton), environmental damage from monoculture incentivized, and overall deadweight loss estimated at $50-120 billion annually. Additional sources: https://farm.ewg.org/ | https://www.ers.usda.gov/topics/farm-economy/farm-sector-income-finances/government-payments-the-safety-net/

Posen, B. R. Restraint: A New Foundation for u.s. Grand Strategy. (Posen, 2014).

The United States could maintain adequate deterrence and defense with a much smaller military budget. Current spending levels reflect force projection capabilities far beyond what homeland security and deterrence require. A strategy of restraint could reduce defense spending by 40-50% while maintaining security through nuclear deterrence and geographic advantages. Additional sources: https://www.cornellpress.cornell.edu/book/9780801452581/restraint/

Alliance, D. P. The drug war by the numbers. (2021)

Since 1971, the war on drugs has cost the United States an estimated $1 trillion in enforcement. The federal drug control budget was $41 billion in 2022. Mass incarceration costs the U.S. at least $182 billion every year, with over $450 billion spent to incarcerate individuals on drug charges in federal prisons.

Crawford, N. C. & Lutz, C. Blood and treasure: United states budgetary costs and human costs of 20 years of war. (2023)

The total costs of the post-9/11 wars in Iraq, Afghanistan, Pakistan, and Syria are expected to exceed $8 trillion. This includes $2.89 trillion for Iraq/Syria, veterans care through 2050 projected at more than $2 trillion, and interest on war debt adding $6.5 trillion through 2050.

Fund, I. M. IMF fossil fuel subsidies data: 2023 update. (2023)

Globally, fossil fuel subsidies were $7 trillion in 2022 or 7.1 percent of GDP. The United States subsidies totaled $649 billion. Underpricing for local air pollution costs and climate damages are the largest contributor, accounting for about 30 percent each.

Papanicolas, I. et al. Health care spending in the united states and other high-income countries. Papanicolas et al. https://jamanetwork.com/journals/jama/article-abstract/2674671 (2018)

The US spent approximately twice as much as other high-income countries on medical care (mean per capita: $9,892 vs $5,289), with similar utilization but much higher prices. Administrative costs accounted for 8% of US spending vs 1-3% in other countries. US spending on pharmaceuticals was $1,443 per capita vs $749 elsewhere. Despite spending more, US health outcomes are not better. Additional sources: https://jamanetwork.com/journals/jama/article-abstract/2674671

10.

Hsieh, C.-T. & Moretti, E. Housing constraints and spatial misallocation. Hsieh & Moretti https://www.aeaweb.org/articles?id=10.1257/mac.20170388 (2019)

We quantify the amount of spatial misallocation of labor across US cities and its aggregate costs. Tight land-use restrictions in high-productivity cities like New York, San Francisco, and Boston lowered aggregate US growth by 36% from 1964 to 2009. Local constraints on housing supply have had enormous effects on the national economy. Additional sources: https://www.aeaweb.org/articles?id=10.1257/mac.20170388

11.

Justice, V. I. of. The economic burden of incarceration in the united states. Vera Institute https://www.vera.org/publications/the-economic-burden-of-incarceration-in-the-u-s (2024)

US incarceration costs approximately $80 billion annually in direct correctional expenditures alone. Including social costs (lost earnings, family impacts, health effects, reduced child outcomes), total burden exceeds $300 billion annually. The US incarcerates at 5x the rate of other OECD countries with no corresponding reduction in crime. Evidence shows community-based alternatives cost less and reduce recidivism more effectively. Additional sources: https://www.vera.org/publications/the-economic-burden-of-incarceration-in-the-u-s | https://www.prisonpolicy.org/reports/pie2024.html | https://www.rand.org/pubs/research_reports/RRA108-3.html

12.

Marron Institute, N. Transit costs project - why US infrastructure costs so much. NYU Transit Costs Project https://transitcosts.com/ (2024)

The United States builds transit infrastructure at dramatically higher costs than peer countries. New York’s Second Avenue Subway cost $2.5 billion per kilometer vs. $200-500 million in European cities. US highway construction similarly costs 2-5x more than comparable projects abroad. Causes include: excessive environmental review, litigation risk, lack of in-house expertise, fragmented project management, and inflated soft costs. Additional sources: https://transitcosts.com/ | https://www.brookings.edu/articles/why-does-infrastructure-cost-so-much/

13.

Clemens, M. A. Economics and emigration: Trillion-dollar bills on the sidewalk? Journal of Economic Perspectives 25, 83–106 (2011)

Free global labor mobility would increase gross world product by somewhere in the range of 67-147%... The gains to eliminating migration barriers amount to large fractions of world GDP—one or two orders of magnitude larger than the gains from dropping all remaining restrictions on international flows of goods and capital.

14.

Kleiner, M. M. & Krueger, A. B. Analyzing the extent and influence of occupational licensing on the labor market. Journal of Labor Economics 31, S173–S202 (2013)

Occupational licensing affects 29% of US workers and creates labor market distortions costing 2-3% of GDP.

15.

Lab, Y. B. The fiscal, economic, and distributional effects of all u.s. tariffs. (2025)

Accounting for all the 2025 US tariffs and retaliation implemented to date, the level of real GDP is persistently -0.6% smaller in the long run, the equivalent of $160 billion 2024$ annually.

16.

Foundation, T. Tax compliance costs the US economy $546 billion annually. https://taxfoundation.org/data/all/federal/irs-tax-compliance-costs/ (2024)

Americans will spend over 7.9 billion hours complying with IRS tax filing and reporting requirements in 2024. This costs the economy roughly $413 billion in lost productivity. In addition, the IRS estimates that Americans spend roughly $133 billion annually in out-of-pocket costs, bringing the total compliance costs to $546 billion, or nearly 2 percent of GDP.

17.

Organization, W. H. WHO global health estimates 2024. World Health Organization https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates (2024)

Comprehensive mortality and morbidity data by cause, age, sex, country, and year Global mortality: 55-60 million deaths annually Lives saved by modern medicine (vaccines, cardiovascular drugs, oncology): 12M annually (conservative aggregate) Leading causes of death: Cardiovascular disease (17.9M), Cancer (10.3M), Respiratory disease (4.0M) Note: Baseline data for regulatory mortality analysis. Conservative estimate of pharmaceutical impact based on WHO immunization data (4.5M/year from vaccines) + cardiovascular interventions (3.3M/year) + oncology (1.5M/year) + other therapies. Additional sources: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates

18.

GiveWell. GiveWell cost per life saved for top charities (2024). GiveWell: Top Charities https://www.givewell.org/charities/top-charities

General range: $3,000-$5,500 per life saved (GiveWell top charities) Helen Keller International (Vitamin A): $3,500 average (2022-2024); varies $1,000-$8,500 by country Against Malaria Foundation: $5,500 per life saved New Incentives (vaccination incentives): $4,500 per life saved Malaria Consortium (seasonal malaria chemoprevention): $3,500 per life saved VAS program details: $2 to provide vitamin A supplements to child for one year Note: Figures accurate for 2024. Helen Keller VAS program has wide country variation ($1K-$8.5K) but $3,500 is accurate average. Among most cost-effective interventions globally Additional sources: https://www.givewell.org/charities/top-charities | https://www.givewell.org/charities/helen-keller-international | https://ourworldindata.org/cost-effectiveness

19.

AARP. Unpaid caregiver hours and economic value. AARP 2023 https://www.aarp.org/caregiving/financial-legal/info-2023/unpaid-caregivers-provide-billions-in-care.html (2023)

Average family caregiver: 25-26 hours per week (100-104 hours per month) 38 million caregivers providing 36 billion hours of care annually Economic value: $16.59 per hour = $600 billion total annual value (2021) 28% of people provided eldercare on a given day, averaging 3.9 hours when providing care Caregivers living with care recipient: 37.4 hours per week Caregivers not living with recipient: 23.7 hours per week Note: Disease-related caregiving is subset of total; includes elderly care, disability care, and child care Additional sources: https://www.aarp.org/caregiving/financial-legal/info-2023/unpaid-caregivers-provide-billions-in-care.html | https://www.bls.gov/news.release/elcare.nr0.htm | https://www.caregiver.org/resource/caregiver-statistics-demographics/

20.

MMWR, C. Childhood vaccination economic benefits. CDC MMWR https://www.cdc.gov/mmwr/volumes/73/wr/mm7331a2.htm (1994)

US programs (1994-2023): $540B direct savings, $2.7T societal savings ( $18B/year direct, $90B/year societal) Global (2001-2020): $820B value for 10 diseases in 73 countries ( $41B/year) ROI: $11 return per $1 invested Measles vaccination alone saved 93.7M lives (61% of 154M total) over 50 years (1974-2024) Additional sources: https://www.cdc.gov/mmwr/volumes/73/wr/mm7331a2.htm | https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24

21.

CDC. Childhood vaccination (US) ROI. CDC https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6316a4.htm (2017).

22.

Labor Statistics, U. S. B. of. CPI inflation calculator. (2024)

CPI-U (1980): 82.4 CPI-U (2024): 313.5 Inflation multiplier (1980-2024): 3.80× Cumulative inflation: 280.48% Average annual inflation rate: 3.08% Note: Official U.S. government inflation data using Consumer Price Index for All Urban Consumers (CPI-U). Additional sources: https://www.bls.gov/data/inflation_calculator.htm

23.

Del Rosal, I. The empirical measurement of rent-seeking costs. Journal of Economic Surveys https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-6419.2009.00621.x (2011)

A comprehensive survey of empirical estimates finds rent-seeking costs range from 0.2% to 23.7% of GDP across different methodologies and countries. Laband & Sophocleus (1988) estimated up to 45% for the US.

24.

via, D. analysis. ClinicalTrials.gov cumulative enrollment data (2025). Direct analysis via ClinicalTrials.gov API v2 https://clinicaltrials.gov/data-api/api

Analysis of 100,000 active/recruiting/completed trials on ClinicalTrials.gov (November 2025) shows cumulative enrollment of 12.2 million participants: Phase 1 (722k), Phase 2 (2.2M), Phase 3 (6.5M), Phase 4 (2.7M). Median participants per trial: Phase 1 (33), Phase 2 (60), Phase 3 (237), Phase 4 (90). Additional sources: https://clinicaltrials.gov/data-api/api

25.

CAN, A. Clinical trial patient participation rate. ACS CAN: Barriers to Clinical Trial Enrollment https://www.fightcancer.org/policy-resources/barriers-patient-enrollment-therapeutic-clinical-trials-cancer

Only 3-5% of adult cancer patients in US receive treatment within clinical trials About 5% of American adults have ever participated in any clinical trial Oncology: 2-3% of all oncology patients participate Contrast: 50-60% enrollment for pediatric cancer trials (<15 years old) Note: 20% of cancer trials fail due to insufficient enrollment; 11% of research sites enroll zero patients Additional sources: https://www.fightcancer.org/policy-resources/barriers-patient-enrollment-therapeutic-clinical-trials-cancer | https://hints.cancer.gov/docs/Briefs/HINTS_Brief_48.pdf

26.

ScienceDaily. Global prevalence of chronic disease. ScienceDaily: GBD 2015 Study https://www.sciencedaily.com/releases/2015/06/150608081753.htm (2015)

2.3 billion individuals had more than five ailments (2013) Chronic conditions caused 74% of all deaths worldwide (2019), up from 67% (2010) Approximately 1 in 3 adults suffer from multiple chronic conditions (MCCs) Risk factor exposures: 2B exposed to biomass fuel, 1B to air pollution, 1B smokers Projected economic cost: $47 trillion by 2030 Note: 2.3B with 5+ ailments is more accurate than "2B with chronic disease." One-third of all adults globally have multiple chronic conditions Additional sources: https://www.sciencedaily.com/releases/2015/06/150608081753.htm | https://pmc.ncbi.nlm.nih.gov/articles/PMC10830426/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC6214883/

27.

C&EN. Annual number of new drugs approved globally: 50. C&EN https://cen.acs.org/pharmaceuticals/50-new-drugs-received-FDA/103/i2 (2025)

50 new drugs approved annually Additional sources: https://cen.acs.org/pharmaceuticals/50-new-drugs-received-FDA/103/i2 | https://www.fda.gov/drugs/development-approval-process-drugs/novel-drug-approvals-fda

28.

estimates, I. Clinical trial abandonment.

Average: 10% abandoned before completion

29.

Report, I. Global trial capacity. IQVIA Report: Clinical Trial Subjects Number Drops Due to Decline in COVID-19 Enrollment https://gmdpacademy.org/news/iqvia-report-clinical-trial-subjects-number-drops-due-to-decline-in-covid-19-enrollment/

1.9M participants annually (2022, post-COVID normalization from 4M peak in 2021) Additional sources: https://gmdpacademy.org/news/iqvia-report-clinical-trial-subjects-number-drops-due-to-decline-in-covid-19-enrollment/

30.

Research & Markets. Global clinical trials market 2024. Research and Markets https://www.globenewswire.com/news-release/2024/04/19/2866012/0/en/Global-Clinical-Trials-Market-Research-Report-2024-An-83-16-Billion-Market-by-2030-AI-Machine-Learning-and-Blockchain-will-Transform-the-Clinical-Trials-Landscape.html (2024)

Global clinical trials market valued at approximately $83 billion in 2024, with projections to reach $83-132 billion by 2030. Additional sources: https://www.globenewswire.com/news-release/2024/04/19/2866012/0/en/Global-Clinical-Trials-Market-Research-Report-2024-An-83-16-Billion-Market-by-2030-AI-Machine-Learning-and-Blockchain-will-Transform-the-Clinical-Trials-Landscape.html | https://www.precedenceresearch.com/clinical-trials-market

31.

OpenSecrets. Lobbying spend (defense). OpenSecrets https://www.opensecrets.org/federal-lobbying/industries/summary?cycle=2024\&id=D (2024).

32.

GiveWell. Cost per DALY for deworming programs. https://www.givewell.org/international/technical/programs/deworming/cost-effectiveness

Schistosomiasis treatment: $28.19-$70.48 per DALY (using arithmetic means with varying disability weights) Soil-transmitted helminths (STH) treatment: $82.54 per DALY (midpoint estimate) Note: GiveWell explicitly states this 2011 analysis is "out of date" and their current methodology focuses on long-term income effects rather than short-term health DALYs Additional sources: https://www.givewell.org/international/technical/programs/deworming/cost-effectiveness

33.

Numbers, T. by. Pre-1962 drug development costs and timeline (think by numbers). Think by Numbers: How Many Lives Does FDA Save? https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ (1962)

Historical estimates (1970-1985): USD $226M fully capitalized (2011 prices) 1980s drugs: $65M after-tax R&D (1990 dollars), $194M compounded to approval (1990 dollars) Modern comparison: $2-3B costs, 7-12 years (dramatic increase from pre-1962) Context: 1962 regulatory clampdown reduced new treatment production by 70%, dramatically increasing development timelines and costs Note: Secondary source; less reliable than Congressional testimony Additional sources: https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ | https://en.wikipedia.org/wiki/Cost_of_drug_development | https://www.statnews.com/2018/10/01/changing-1962-law-slash-drug-prices/

34.

(BIO), B. I. O. BIO clinical development success rates 2011-2020. Biotechnology Innovation Organization (BIO) https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf (2021)

Phase I duration: 2.3 years average Total time to market (Phase I-III + approval): 10.5 years average Phase transition success rates: Phase I→II: 63.2%, Phase II→III: 30.7%, Phase III→Approval: 58.1% Overall probability of approval from Phase I: 12% Note: Largest publicly available study of clinical trial success rates. Efficacy lag = 10.5 - 2.3 = 8.2 years post-safety verification. Additional sources: https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf

35.

Medicine, N. Drug repurposing rate ( 30%). Nature Medicine https://www.nature.com/articles/s41591-024-03233-x (2024)

Approximately 30% of drugs gain at least one new indication after initial approval. Additional sources: https://www.nature.com/articles/s41591-024-03233-x

36.

EPI. Education investment economic multiplier (2.1). EPI: Public Investments Outside Core Infrastructure https://www.epi.org/publication/bp348-public-investments-outside-core-infrastructure/

Early childhood education: Benefits 12X outlays by 2050; $8.70 per dollar over lifetime Educational facilities: $1 spent → $1.50 economic returns Energy efficiency comparison: 2-to-1 benefit-to-cost ratio (McKinsey) Private return to schooling: 9% per additional year (World Bank meta-analysis) Note: 2.1 multiplier aligns with benefit-to-cost ratios for educational infrastructure/energy efficiency. Early childhood education shows much higher returns (12X by 2050) Additional sources: https://www.epi.org/publication/bp348-public-investments-outside-core-infrastructure/ | https://documents1.worldbank.org/curated/en/442521523465644318/pdf/WPS8402.pdf | https://freopp.org/whitepapers/establishing-a-practical-return-on-investment-framework-for-education-and-skills-development-to-expand-economic-opportunity/

37.

PMC. Healthcare investment economic multiplier (1.8). PMC: California Universal Health Care https://pmc.ncbi.nlm.nih.gov/articles/PMC5954824/ (2022)

Healthcare fiscal multiplier: 4.3 (95% CI: 2.5-6.1) during pre-recession period (1995-2007) Overall government spending multiplier: 1.61 (95% CI: 1.37-1.86) Why healthcare has high multipliers: No effect on trade deficits (spending stays domestic); improves productivity & competitiveness; enhances long-run potential output Gender-sensitive fiscal spending (health & care economy) produces substantial positive growth impacts Note: "1.8" appears to be conservative estimate; research shows healthcare multipliers of 4.3 Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC5954824/ | https://cepr.org/voxeu/columns/government-investment-and-fiscal-stimulus | https://ncbi.nlm.nih.gov/pmc/articles/PMC3849102/ | https://set.odi.org/wp-content/uploads/2022/01/Fiscal-multipliers-review.pdf

38.

Bank, W. Infrastructure investment economic multiplier (1.6). World Bank: Infrastructure Investment as Stimulus https://blogs.worldbank.org/en/ppps/effectiveness-infrastructure-investment-fiscal-stimulus-what-weve-learned (2022)

Infrastructure fiscal multiplier: 1.6 during contractionary phase of economic cycle Average across all economic states: 1.5 (meaning $1 of public investment → $1.50 of economic activity) Time horizon: 0.8 within 1 year, 1.5 within 2-5 years Range of estimates: 1.5-2.0 (following 2008 financial crisis & American Recovery Act) Italian public construction: 1.5-1.9 multiplier US ARRA: 0.4-2.2 range (differential impacts by program type) Economic Policy Institute: Uses 1.6 for infrastructure spending (middle range of estimates) Note: Public investment less likely to crowd out private activity during recessions; particularly effective when monetary policy loose with near-zero rates Additional sources: https://blogs.worldbank.org/en/ppps/effectiveness-infrastructure-investment-fiscal-stimulus-what-weve-learned | https://www.gihub.org/infrastructure-monitor/insights/fiscal-multiplier-effect-of-infrastructure-investment/ | https://cepr.org/voxeu/columns/government-investment-and-fiscal-stimulus | https://www.richmondfed.org/publications/research/economic_brief/2022/eb_22-04

39.

Mercatus. Military spending economic multiplier (0.6). Mercatus: Defense Spending and Economy https://www.mercatus.org/research/research-papers/defense-spending-and-economy

Ramey (2011): 0.6 short-run multiplier Barro (1981): 0.6 multiplier for WWII spending (war spending crowded out 40¢ private economic activity per federal dollar) Barro & Redlick (2011): 0.4 within current year, 0.6 over two years; increased govt spending reduces private-sector GDP portions General finding: $1 increase in deficit-financed federal military spending = less than $1 increase in GDP Variation by context: Central/Eastern European NATO: 0.6 on impact, 1.5-1.6 in years 2-3, gradual fall to zero Ramey & Zubairy (2018): Cumulative 1% GDP increase in military expenditure raises GDP by 0.7% Additional sources: https://www.mercatus.org/research/research-papers/defense-spending-and-economy | https://cepr.org/voxeu/columns/world-war-ii-america-spending-deficits-multipliers-and-sacrifice | https://www.rand.org/content/dam/rand/pubs/research_reports/RRA700/RRA739-2/RAND_RRA739-2.pdf

40.

FDA. FDA-approved prescription drug products (20,000+). FDA https://www.fda.gov/media/143704/download

There are over 20,000 prescription drug products approved for marketing. Additional sources: https://www.fda.gov/media/143704/download

41.

FDA. FDA GRAS list count ( 570-700). FDA https://www.fda.gov/food/generally-recognized-safe-gras/gras-notice-inventory

The FDA GRAS (Generally Recognized as Safe) list contains approximately 570–700 substances. Additional sources: https://www.fda.gov/food/generally-recognized-safe-gras/gras-notice-inventory

42.

ACLED. Active combat deaths annually. ACLED: Global Conflict Surged 2024 https://acleddata.com/2024/12/12/data-shows-global-conflict-surged-in-2024-the-washington-post/ (2024)

2024: 233,597 deaths (30% increase from 179,099 in 2023) Deadliest conflicts: Ukraine (67,000), Palestine (35,000) Nearly 200,000 acts of violence (25% higher than 2023, double from 5 years ago) One in six people globally live in conflict-affected areas Additional sources: https://acleddata.com/2024/12/12/data-shows-global-conflict-surged-in-2024-the-washington-post/ | https://acleddata.com/media-citation/data-shows-global-conflict-surged-2024-washington-post | https://acleddata.com/conflict-index/index-january-2024/

43.

UCDP. State violence deaths annually. UCDP: Uppsala Conflict Data Program https://ucdp.uu.se/

Uppsala Conflict Data Program (UCDP): Tracks one-sided violence (organized actors attacking unarmed civilians) UCDP definition: Conflicts causing at least 25 battle-related deaths in calendar year 2023 total organized violence: 154,000 deaths; Non-state conflicts: 20,900 deaths UCDP collects data on state-based conflicts, non-state conflicts, and one-sided violence Specific "2,700 annually" figure for state violence not found in recent UCDP data; actual figures vary annually Additional sources: https://ucdp.uu.se/ | https://en.wikipedia.org/wiki/Uppsala_Conflict_Data_Program | https://ourworldindata.org/grapher/deaths-in-armed-conflicts-by-region

44.

Data, O. W. in. Terror attack deaths (8,300 annually). Our World in Data: Terrorism https://ourworldindata.org/terrorism (2024)

2023: 8,352 deaths (22% increase from 2022, highest since 2017) 2023: 3,350 terrorist incidents (22% decrease), but 56% increase in avg deaths per attack Global Terrorism Database (GTD): 200,000+ terrorist attacks recorded (2021 version) Maintained by: National Consortium for Study of Terrorism & Responses to Terrorism (START), U. of Maryland Geographic shift: Epicenter moved from Middle East to Central Sahel (sub-Saharan Africa) - now >50% of all deaths Additional sources: https://ourworldindata.org/terrorism | https://reliefweb.int/report/world/global-terrorism-index-2024 | https://www.start.umd.edu/gtd/ | https://ourworldindata.org/grapher/fatalities-from-terrorism

45.

Health Metrics, I. for & (IHME), E. IHME global burden of disease 2021 (2.88B DALYs, 1.13B YLD). Institute for Health Metrics and Evaluation (IHME) https://vizhub.healthdata.org/gbd-results/ (2024)

In 2021, global DALYs totaled approximately 2.88 billion, comprising 1.75 billion Years of Life Lost (YLL) and 1.13 billion Years Lived with Disability (YLD). This represents a 13% increase from 2019 (2.55B DALYs), largely attributable to COVID-19 deaths and aging populations. YLD accounts for approximately 39% of total DALYs, reflecting the substantial burden of non-fatal chronic conditions. Additional sources: https://vizhub.healthdata.org/gbd-results/ | https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24 | https://www.healthdata.org/research-analysis/about-gbd

46.

War, B. W. C. of. Environmental cost of war ($100B annually). Brown Watson Costs of War: Environmental Cost https://watson.brown.edu/costsofwar/costs/social/environment

War on Terror emissions: 1.2B metric tons GHG (equivalent to 257M cars/year) Military: 5.5% of global GHG emissions (2X aviation + shipping combined) US DoD: World’s single largest institutional oil consumer, 47th largest emitter if nation Cleanup costs: $500B+ for military contaminated sites Gaza war environmental damage: $56.4B; landmine clearance: $34.6B expected Climate finance gap: Rich nations spend 30X more on military than climate finance Note: Military activities cause massive environmental damage through GHG emissions, toxic contamination, and long-term cleanup costs far exceeding current climate finance commitments Additional sources: https://watson.brown.edu/costsofwar/costs/social/environment | https://earth.org/environmental-costs-of-wars/ | https://transformdefence.org/transformdefence/stats/

47.

ScienceDaily. Medical research lives saved annually (4.2 million). ScienceDaily: Physical Activity Prevents 4M Deaths https://www.sciencedaily.com/releases/2020/06/200617194510.htm (2020)

Physical activity: 3.9M early deaths averted annually worldwide (15% lower premature deaths than without) COVID vaccines (2020-2024): 2.533M deaths averted, 14.8M life-years preserved; first year alone: 14.4M deaths prevented Cardiovascular prevention: 3 interventions could delay 94.3M deaths over 25 years (antihypertensives alone: 39.4M) Pandemic research response: Millions of deaths averted through rapid vaccine/drug development Additional sources: https://www.sciencedaily.com/releases/2020/06/200617194510.htm | https://pmc.ncbi.nlm.nih.gov/articles/PMC9537923/ | https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.118.038160 | https://pmc.ncbi.nlm.nih.gov/articles/PMC9464102/

48.

SIPRI. 36:1 disparity ratio of spending on weapons over cures. SIPRI: Military Spending https://www.sipri.org/commentary/blog/2016/opportunity-cost-world-military-spending (2016)

Global military spending: $2.7 trillion (2024, SIPRI) Global government medical research: $68 billion (2024) Actual ratio: 39.7:1 in favor of weapons over medical research Military R&D alone: $85B (2004 data, 10% of global R&D) Military spending increases crowd out health: 1% ↑ military = 0.62% ↓ health spending Note: Ratio actually worse than 36:1. Each 1% increase in military spending reduces health spending by 0.62%, with effect more intense in poorer countries (0.962% reduction) Additional sources: https://www.sipri.org/commentary/blog/2016/opportunity-cost-world-military-spending | https://pmc.ncbi.nlm.nih.gov/articles/PMC9174441/ | https://www.congress.gov/crs-product/R45403

49.

Numbers, T. by. Lost human capital due to war ($270B annually). Think by Numbers: War Costs $74 <https://thinkbynumbers.org/military/war/the-economic-case-for-peace-a-comprehensive-financial-analysis/> (2021)

Lost human capital from war: $300B annually (economic impact of losing skilled/productive individuals to conflict) Broader conflict/violence cost: $14T/year globally 1.4M violent deaths/year; conflict holds back economic development, causes instability, widens inequality, erodes human capital 2002: 48.4M DALYs lost from 1.6M violence deaths = $151B economic value (2000 USD) Economic toll includes: commodity prices, inflation, supply chain disruption, declining output, lost human capital Additional sources: <https://thinkbynumbers.org/military/war/the-economic-case-for-peace-a-comprehensive-financial-analysis/> | https://www.weforum.org/stories/2021/02/war-violence-costs-each-human-5-a-day/ | https://pubmed.ncbi.nlm.nih.gov/19115548/

50.

PubMed. Psychological impact of war cost ($100B annually). PubMed: Economic Burden of PTSD https://pubmed.ncbi.nlm.nih.gov/35485933/

PTSD economic burden (2018 U.S.): $232.2B total ($189.5B civilian, $42.7B military) Civilian costs driven by: Direct healthcare ($66B), unemployment ($42.7B) Military costs driven by: Disability ($17.8B), direct healthcare ($10.1B) Exceeds costs of other mental health conditions (anxiety, depression) War-exposed populations: 2-3X higher rates of anxiety, depression, PTSD; women and children most vulnerable Note: Actual burden $232B, significantly higher than "$100B" claimed Additional sources: https://pubmed.ncbi.nlm.nih.gov/35485933/ | https://news.va.gov/103611/study-national-economic-burden-of-ptsd-staggering/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC9957523/

51.

CGDev. UNHCR average refugee support cost. CGDev https://www.cgdev.org/blog/costs-hosting-refugees-oecd-countries-and-why-uk-outlier (2024)

The average cost of supporting a refugee is $1,384 per year. This represents total host country costs (housing, healthcare, education, security). OECD countries average $6,100 per refugee (mean 2022-2023), with developing countries spending $700-1,000. Global weighted average of $1,384 is reasonable given that 75-85% of refugees are in low/middle-income countries. Additional sources: https://www.cgdev.org/blog/costs-hosting-refugees-oecd-countries-and-why-uk-outlier | https://www.unhcr.org/sites/default/files/2024-11/UNHCR-WB-global-cost-of-refugee-inclusion-in-host-country-health-systems.pdf

52.

Bank, W. World bank trade disruption cost from conflict. World Bank https://www.worldbank.org/en/topic/trade/publication/trading-away-from-conflict

Estimated $616B annual cost from conflict-related trade disruption. World Bank research shows civil war costs an average developing country 30 years of GDP growth, with 20 years needed for trade to return to pre-war levels. Trade disputes analysis shows tariff escalation could reduce global exports by up to $674 billion. Additional sources: https://www.worldbank.org/en/topic/trade/publication/trading-away-from-conflict | https://www.nber.org/papers/w11565 | http://blogs.worldbank.org/en/trade/impacts-global-trade-and-income-current-trade-disputes

53.

VA. Veteran healthcare cost projections. VA https://department.va.gov/wp-content/uploads/2025/06/2026-Budget-in-Brief.pdf (2026)

VA budget: $441.3B requested for FY 2026 (10% increase). Disability compensation: $165.6B in FY 2024 for 6.7M veterans. PACT Act projected to increase spending by $300B between 2022-2031. Costs under Toxic Exposures Fund: $20B (2024), $30.4B (2025), $52.6B (2026). Additional sources: https://department.va.gov/wp-content/uploads/2025/06/2026-Budget-in-Brief.pdf | https://www.cbo.gov/publication/45615 | https://www.legion.org/information-center/news/veterans-healthcare/2025/june/va-budget-tops-400-billion-for-2025-from-higher-spending-on-mandated-benefits-medical-care

54.

IQVIA Institute for Human Data Science. The global use of medicines 2024: Outlook to 2028. IQVIA Institute Report https://www.iqvia.com/insights/the-iqvia-institute/reports-and-publications/reports/the-global-use-of-medicines-2024-outlook-to-2028 (2024)

Global days of therapy reached 1.8 trillion in 2019 (234 defined daily doses per person). Diabetes, respiratory, CVD, and cancer account for 71 percent of medicine use. Projected to reach 3.8 trillion DDDs by 2028.

55.

size, D. from global market & ratios, public/private funding. Private industry clinical trial spending.

Private pharmaceutical and biotech industry spends approximately $75-90 billion annually on clinical trials, representing roughly 90% of global clinical trial spending.

56.

IHME Global Burden of Disease (2.55B DALYs), C. from & GDP per capita valuation, global. $109 trillion annual global disease burden.

The global economic burden of disease, including direct healthcare costs ($8.2 trillion) and lost productivity ($100.9 trillion from 2.55 billion DALYs × $39,570 per DALY), totals approximately$109.1 trillion annually.

57.

Trials, A. C. Global government spending on interventional clinical trials: $3-6 billion/year. Applied Clinical Trials https://www.appliedclinicaltrialsonline.com/view/sizing-clinical-research-market

Estimated range based on NIH ( $0.8-5.6B), NIHR ($1.6B total budget), and EU funding ( $1.3B/year). Roughly 5-10% of global market. Additional sources: https://www.appliedclinicaltrialsonline.com/view/sizing-clinical-research-market | https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(20

58.

Suisse/UBS, C. Credit suisse global wealth report 2023. Credit Suisse/UBS https://www.ubs.com/global/en/family-office-uhnw/reports/global-wealth-report-2023.html (2023)

Total global household wealth: USD 454.4 trillion (2022) Wealth declined by USD 11.3 trillion (-2.4%) in 2022, first decline since 2008 Wealth per adult: USD 84,718 Additional sources: https://www.ubs.com/global/en/family-office-uhnw/reports/global-wealth-report-2023.html

59.

budgets:, S. component country. Global government medical research spending ($67.5B, 2023–2024). See component country budgets: NIH Budget #nih-budget-fy2025.

60.

SIPRI. Global military spending ($2.72T, 2024). SIPRI https://www.sipri.org/publications/2025/sipri-fact-sheets/trends-world-military-expenditure-2024 (2025).

61.

budgets, E. from major foundation & activities. Nonprofit clinical trial funding estimate.

Nonprofit foundations spend an estimated $2-5 billion annually on clinical trials globally, representing approximately 2-5% of total clinical trial spending.

62.

IQVIA, I. reports: Global pharmaceutical r&d spending.

Total global pharmaceutical R&D spending is approximately $300 billion annually. Clinical trials represent 15-20% of this total ($45-60B), with the remainder going to drug discovery, preclinical research, regulatory affairs, and manufacturing development.

63.

UN. Global population reaches 8 billion. UN: World Population 8 Billion Nov 15 2022 https://www.un.org/en/desa/world-population-reach-8-billion-15-november-2022 (2022)

Milestone: November 15, 2022 (UN World Population Prospects 2022) Day of Eight Billion" designated by UN Added 1 billion people in just 11 years (2011-2022) Growth rate: Slowest since 1950; fell under 1% in 2020 Future: 15 years to reach 9B (2037); projected peak 10.4B in 2080s Projections: 8.5B (2030), 9.7B (2050), 10.4B (2080-2100 plateau) Note: Milestone reached Nov 2022. Population growth slowing; will take longer to add next billion (15 years vs 11 years) Additional sources: https://www.un.org/en/desa/world-population-reach-8-billion-15-november-2022 | https://www.un.org/en/dayof8billion | https://en.wikipedia.org/wiki/Day_of_Eight_Billion

64.

School, H. K. 3.5% participation tipping point. Harvard Kennedy School https://www.hks.harvard.edu/centers/carr/publications/35-rule-how-small-minority-can-change-world (2020)

The research found that nonviolent campaigns were twice as likely to succeed as violent ones, and once 3.5% of the population were involved, they were always successful. Chenoweth and Maria Stephan studied the success rates of civil resistance efforts from 1900 to 2006, finding that nonviolent movements attracted, on average, four times as many participants as violent movements and were more likely to succeed. Key finding: Every campaign that mobilized at least 3.5% of the population in sustained protest was successful (in their 1900-2006 dataset) Note: The 3.5% figure is a descriptive statistic from historical analysis, not a guaranteed threshold. One exception (Bahrain 2011-2014 with 6%+ participation) has been identified. The rule applies to regime change, not policy change in democracies. Additional sources: https://www.hks.harvard.edu/centers/carr/publications/35-rule-how-small-minority-can-change-world | https://www.hks.harvard.edu/sites/default/files/2024-05/Erica%20Chenoweth_2020-005.pdf | https://www.bbc.com/future/article/20190513-it-only-takes-35-of-people-to-change-the-world | https://en.wikipedia.org/wiki/3.5%25_rule

65.

NHGRI. Human genome project and CRISPR discovery. NHGRI https://www.genome.gov/11006929/2003-release-international-consortium-completes-hgp (2003)

Your DNA is 3 billion base pairs Read the entire code (Human Genome Project, completed 2003) Learned to edit it (CRISPR, discovered 2012) Additional sources: https://www.genome.gov/11006929/2003-release-international-consortium-completes-hgp | https://www.nobelprize.org/prizes/chemistry/2020/press-release/

66.

PMC. Only 12% of human interactome targeted. PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC10749231/ (2023)

Mapping 350,000+ clinical trials showed that only 12% of the human interactome has ever been targeted by drugs. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10749231/

67.

WHO. ICD-10 code count ( 14,000). WHO https://icd.who.int/browse10/2019/en (2019)

The ICD-10 classification contains approximately 14,000 codes for diseases, signs and symptoms. Additional sources: https://icd.who.int/browse10/2019/en

68.

Wikipedia. Longevity escape velocity (LEV) - maximum human life extension potential. Wikipedia: Longevity Escape Velocity https://en.wikipedia.org/wiki/Longevity_escape_velocity

Longevity escape velocity: Hypothetical point where medical advances extend life expectancy faster than time passes Term coined by Aubrey de Grey (biogerontologist) in 2004 paper; concept from David Gobel (Methuselah Foundation) Current progress: Science adds 3 months to lifespan per year; LEV requires adding >1 year per year Sinclair (Harvard): "There is no biological upper limit to age" - first person to live to 150 may already be born De Grey: 50% chance of reaching LEV by mid-to-late 2030s; SENS approach = damage repair rather than slowing damage Kurzweil (2024): LEV by 2029-2035, AI will simulate biological processes to accelerate solutions George Church: LEV "in a decade or two" via age-reversal clinical trials Natural lifespan cap: 120-150 years (Jeanne Calment record: 122); engineering approach could bypass via damage repair Key mechanisms: Epigenetic reprogramming, senolytic drugs, stem cell therapy, gene therapy, AI-driven drug discovery Current record: Jeanne Calment (122 years, 164 days) - record unbroken since 1997 Note: LEV is theoretical but increasingly plausible given demonstrated age reversal in mice (109% lifespan extension) and human cells (30-year epigenetic age reversal) Additional sources: https://en.wikipedia.org/wiki/Longevity_escape_velocity | https://pmc.ncbi.nlm.nih.gov/articles/PMC423155/ | https://www.popularmechanics.com/science/a36712084/can-science-cure-death-longevity/ | https://www.diamandis.com/blog/longevity-escape-velocity

69.

OpenSecrets. Lobbyist statistics for washington d.c. OpenSecrets: Lobbying in US https://en.wikipedia.org/wiki/Lobbying_in_the_United_States

Registered lobbyists: Over 12,000 (some estimates); 12,281 registered (2013) Former government employees as lobbyists: 2,200+ former federal employees (1998-2004), including 273 former White House staffers, 250 former Congress members & agency heads Congressional revolving door: 43% (86 of 198) lawmakers who left 1998-2004 became lobbyists; currently 59% leaving to private sector work for lobbying/consulting firms/trade groups Executive branch: 8% were registered lobbyists at some point before/after government service Additional sources: https://en.wikipedia.org/wiki/Lobbying_in_the_United_States | https://www.opensecrets.org/revolving-door | https://www.citizen.org/article/revolving-congress/ | https://www.propublica.org/article/we-found-a-staggering-281-lobbyists-whove-worked-in-the-trump-administration

70.

Vaccines, M. Measles vaccination ROI. MDPI Vaccines https://www.mdpi.com/2076-393X/12/11/1210 (2024)

Single measles vaccination: 167:1 benefit-cost ratio. MMR (measles-mumps-rubella) vaccination: 14:1 ROI. Historical US elimination efforts (1966-1974): benefit-cost ratio of 10.3:1 with net benefits exceeding USD 1.1 billion (1972 dollars, or USD 8.0 billion in 2023 dollars). 2-dose MMR programs show direct benefit/cost ratio of 14.2 with net savings of $5.3 billion, and 26.0 from societal perspectives with net savings of $11.6 billion. Additional sources: https://www.mdpi.com/2076-393X/12/11/1210 | https://www.tandfonline.com/doi/full/10.1080/14760584.2024.2367451

71.

Organization, W. H. Mental health global burden. World Health Organization https://www.who.int/news/item/28-09-2001-the-world-health-report-2001-mental-disorders-affect-one-in-four-people (2022)

One in four people in the world will be affected by mental or neurological disorders at some point in their lives, representing [approximately] 30% of the global burden of disease. Additional sources: https://www.who.int/news/item/28-09-2001-the-world-health-report-2001-mental-disorders-affect-one-in-four-people

72.

Institute, S. I. P. R. Trends in world military expenditure, 2023. (2024).

73.

Orphanet Journal of Rare Diseases (2024), C. from. Diseases getting first effective treatment each year. Calculated from Orphanet Journal of Rare Diseases (2024) https://ojrd.biomedcentral.com/articles/10.1186/s13023-024-03398-1 (2024)

Under the current system, approximately 10-15 diseases per year receive their FIRST effective treatment. Calculation: 5% of 7,000 rare diseases ( 350) have FDA-approved treatment, accumulated over 40 years of the Orphan Drug Act = 9 rare diseases/year. Adding 5-10 non-rare diseases that get first treatments yields 10-20 total. FDA approves 50 drugs/year, but many are for diseases that already have treatments (me-too drugs, second-line therapies). Only 15 represent truly FIRST treatments for previously untreatable conditions.

74.

NIH. NIH budget (FY 2025). NIH https://www.nih.gov/about-nih/organization/budget (2024)

The budget total of $47.7 billion also includes$1.412 billion derived from PHS Evaluation financing... Additional sources: https://www.nih.gov/about-nih/organization/budget | https://officeofbudget.od.nih.gov/

75.

al., B. et. NIH spending on clinical trials: 3.3%. Bentley et al. https://www.fiercebiotech.com/biotech/nih-spending-clinical-trials-reached-81b-over-decade (2023)

NIH spent $8.1 billion on clinical trials for approved drugs (2010-2019), representing 3.3% of relevant NIH spending. Additional sources: https://www.fiercebiotech.com/biotech/nih-spending-clinical-trials-reached-81b-over-decade | https://www.fiercebiotech.com/biotech/nih-spending-clinical-trials-reached-81b-over-decade

76.

PMC. Standard medical research ROI ($20k-$100k/QALY). PMC: Cost-effectiveness Thresholds Used by Study Authors https://pmc.ncbi.nlm.nih.gov/articles/PMC10114019/ (1990)

Typical cost-effectiveness thresholds for medical interventions in rich countries range from $50,000 to $150,000 per QALY. The Institute for Clinical and Economic Review (ICER) uses a $100,000-$150,000/QALY threshold for value-based pricing. Between 1990-2021, authors increasingly cited $100,000 (47% by 2020-21) or $150,000 (24% by 2020-21) per QALY as benchmarks for cost-effectiveness. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10114019/ | https://icer.org/our-approach/methods-process/cost-effectiveness-the-qaly-and-the-evlyg/

77.

Institute, M. RECOVERY trial 82× cost reduction. Manhattan Institute: Slow Costly Trials https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs

RECOVERY trial: $500 per patient ($20M for 48,000 patients = $417/patient) Typical clinical trial: $41,000 median per-patient cost Cost reduction: 80-82× cheaper ($41,000 ÷ $500 ≈ 82×) Efficiency: $50 per patient per answer (10 therapeutics tested, 4 effective) Dexamethasone estimated to save >630,000 lives Additional sources: https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs | https://pmc.ncbi.nlm.nih.gov/articles/PMC9293394/

78.

Trials. Patient willingness to participate in clinical trials. Trials: Patients’ Willingness Survey https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-015-1105-3

Recent surveys: 49-51% willingness (2020-2022) - dramatic drop from 85% (2019) during COVID-19 pandemic Cancer patients when approached: 88% consented to trials (Royal Marsden Hospital) Study type variation: 44.8% willing for drug trial, 76.2% for diagnostic study Top motivation: "Learning more about my health/medical condition" (67.4%) Top barrier: "Worry about experiencing side effects" (52.6%) Additional sources: https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-015-1105-3 | https://www.appliedclinicaltrialsonline.com/view/industry-forced-to-rethink-patient-participation-in-trials | https://pmc.ncbi.nlm.nih.gov/articles/PMC7183682/

79.

CSDD, T. Cost of drug development.

Various estimates suggest $1.0 - $2.5 billion to bring a new drug from discovery through FDA approval, spread across 10 years. Tufts Center for the Study of Drug Development often cited for $1.0 - $2.6 billion/drug. Industry reports (IQVIA, Deloitte) also highlight $2+ billion figures.

80.

Health, V. in. Average lifetime revenue per successful drug. Value in Health: Sales Revenues for New Therapeutic Agents02754-2/fulltext) https://www.valueinhealthjournal.com/article/S1098-3015(24

Study of 361 FDA-approved drugs from 1995-2014 (median follow-up 13.2 years): Mean lifetime revenue: $15.2 billion per drug Median lifetime revenue: $6.7 billion per drug Revenue after 5 years: $3.2 billion (mean) Revenue after 10 years: $9.5 billion (mean) Revenue after 15 years: $19.2 billion (mean) Distribution highly skewed: top 25 drugs (7%) accounted for 38% of total revenue ($2.1T of $5.5T) Additional sources: https://www.valueinhealthjournal.com/article/S1098-3015(24 | https://www.sciencedirect.com/science/article/pii/S1098301524027542

81.

Lichtenberg, F. R. How many life-years have new drugs saved? A three-way fixed-effects analysis of 66 diseases in 27 countries, 2000-2013. International Health 11, 403–416 (2019)

Using 3-way fixed-effects methodology (disease-country-year) across 66 diseases in 22 countries, this study estimates that drugs launched after 1981 saved 148.7 million life-years in 2013 alone. The regression coefficients for drug launches 0-11 years prior (beta=-0.031, SE=0.008) and 12+ years prior (beta=-0.057, SE=0.013) on years of life lost are highly significant (p<0.0001). Confidence interval for life-years saved: 79.4M-239.8M (95 percent CI) based on propagated standard errors from Table 2.

82.

Deloitte. Pharmaceutical r&d return on investment (ROI). Deloitte: Measuring Pharmaceutical Innovation 2025 https://www.deloitte.com/ch/en/Industries/life-sciences-health-care/research/measuring-return-from-pharmaceutical-innovation.html (2025)

Deloitte’s annual study of top 20 pharma companies by R&D spend (2010-2024): 2024 ROI: 5.9% (second year of growth after decade of decline) 2023 ROI: 4.3% (estimated from trend) 2022 ROI: 1.2% (historic low since study began, 13-year low) 2021 ROI: 6.8% (record high, inflated by COVID-19 vaccines/treatments) Long-term trend: Declining for over a decade before 2023 recovery Average R&D cost per asset: $2.3B (2022), $2.23B (2024) These returns (1.2-5.9% range) fall far below typical corporate ROI targets (15-20%) Additional sources: https://www.deloitte.com/ch/en/Industries/life-sciences-health-care/research/measuring-return-from-pharmaceutical-innovation.html | https://www.prnewswire.com/news-releases/deloittes-13th-annual-pharmaceutical-innovation-report-pharma-rd-return-on-investment-falls-in-post-pandemic-market-301738807.html | https://hitconsultant.net/2023/02/16/pharma-rd-roi-falls-to-lowest-level-in-13-years/

83.

Discovery, N. R. D. Drug trial success rate from phase i to approval. Nature Reviews Drug Discovery: Clinical Success Rates https://www.nature.com/articles/nrd.2016.136 (2016)

Overall Phase I to approval: 10-12.8% (conventional wisdom 10%, studies show 12.8%) Recent decline: Average LOA now 6.7% for Phase I (2014-2023 data) Leading pharma companies: 14.3% average LOA (range 8-23%) Varies by therapeutic area: Oncology 3.4%, CNS/cardiovascular lowest at Phase III Phase-specific success: Phase I 47-54%, Phase II 28-34%, Phase III 55-70% Note: 12% figure accurate for historical average. Recent data shows decline to 6.7%, with Phase II as primary attrition point (28% success) Additional sources: https://www.nature.com/articles/nrd.2016.136 | https://pmc.ncbi.nlm.nih.gov/articles/PMC6409418/ | https://academic.oup.com/biostatistics/article/20/2/273/4817524

84.

SofproMed. Phase 3 cost per trial range. SofproMed https://www.sofpromed.com/how-much-does-a-clinical-trial-cost

Phase 3 clinical trials cost between $20 million and $282 million per trial, with significant variation by therapeutic area and trial complexity. Additional sources: https://www.sofpromed.com/how-much-does-a-clinical-trial-cost | https://www.cbo.gov/publication/57126

85.

PMC. Pragmatic trial cost per patient (median $97). PMC: Costs of Pragmatic Clinical Trials https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/

The median cost per participant was $97 (IQR $19–$478), based on 2015 dollars. Systematic review of 64 embedded pragmatic clinical trials. 25% of trials cost <$19/patient; 10 trials exceeded $1,000/patient. U.S. studies median $187 vs non-U.S. median $27. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/

86.

WHO. Polio vaccination ROI. WHO https://www.who.int/news-room/feature-stories/detail/sustaining-polio-investments-offers-a-high-return (2019)

For every dollar spent, the return on investment is nearly US$ 39." Total investment cost of US$ 7.5 billion generates projected economic and social benefits of US$ 289.2 billion from sustaining polio assets and integrating them into expanded immunization, surveillance and emergency response programmes across 8 priority countries (Afghanistan, Iraq, Libya, Pakistan, Somalia, Sudan, Syria, Yemen). Additional sources: https://www.who.int/news-room/feature-stories/detail/sustaining-polio-investments-offers-a-high-return

87.

Olson, M. Big bills left on the sidewalk: Why some nations are rich, and others poor. Journal of Economic Perspectives 10, 3–24 (1996)

Differences between rich and poor countries are primarily due to institutions and policies, not factors of production.

88.

Hayek, F. A. The use of knowledge in society. American Economic Review 35, 519–530 (1945)

The knowledge of the circumstances which we must make use of never exists in concentrated or integrated form but solely as dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess.

89.

Kydland, F. E. & Prescott, E. C. Rules rather than discretion: The inconsistency of optimal plans. Journal of Political Economy 85, 473–492 (1977)

Time-inconsistency describes situations where, with the passing of time, policies that were determined to be optimal yesterday are no longer perceived to be optimal today and are not implemented... This insight shifted the focus of policy analysis from the study of individual policy decisions to the design of institutions that mitigate the time consistency problem.

90.

ICRC. International campaign to ban landmines (ICBL) - ottawa treaty (1997). ICRC https://www.icrc.org/en/doc/resources/documents/article/other/57jpjn.htm (1997)

ICBL: Founded 1992 by 6 NGOs (Handicap International, Human Rights Watch, Medico International, Mines Advisory Group, Physicians for Human Rights, Vietnam Veterans of America Foundation) Started with ONE staff member: Jody Williams as founding coordinator Grew to 1,000+ organizations in 60 countries by 1997 Ottawa Process: 14 months (October 1996 - December 1997) Convention signed by 122 states on December 3, 1997; entered into force March 1, 1999 Achievement: Nobel Peace Prize 1997 (shared by ICBL and Jody Williams) Government funding context: Canada established $100M CAD Canadian Landmine Fund over 10 years (1997); International donors provided $169M in 1997 for mine action (up from $100M in 1996) Additional sources: https://www.icrc.org/en/doc/resources/documents/article/other/57jpjn.htm | https://en.wikipedia.org/wiki/International_Campaign_to_Ban_Landmines | https://www.nobelprize.org/prizes/peace/1997/summary/ | https://un.org/press/en/1999/19990520.MINES.BRF.html | https://www.the-monitor.org/en-gb/reports/2003/landmine-monitor-2003/mine-action-funding.aspx

91.

OpenSecrets. Revolving door: Former members of congress. (2024)

388 former members of Congress are registered as lobbyists. Nearly 5,400 former congressional staffers have left Capitol Hill to become federal lobbyists in the past 10 years. Additional sources: https://www.opensecrets.org/revolving-door

92.

Kinch, M. S. & Griesenauer, R. H. Lost medicines: A longer view of the pharmaceutical industry with the potential to reinvigorate discovery. Drug Discovery Today 24, 875–880 (2019)

Research identified 1,600+ medicines available in 1962. The 1950s represented industry high-water mark with >30 new products in five of ten years; this rate would not be replicated until late 1990s. More than half (880) of these medicines were lost following implementation of Kefauver-Harris Amendment. The peak of 1962 would not be seen again until early 21st century. By 2016 number of organizations actively involved in R&D at level not seen since 1914.

93.

Wikipedia. US military spending reduction after WWII. Wikipedia https://en.wikipedia.org/wiki/Demobilization_of_United_States_Armed_Forces_after_World_War_II (2020)

Peaking at over $81 billion in 1945, the U.S. military budget plummeted to approximately $13 billion by 1948, representing an 84% decrease. The number of personnel was reduced almost 90%, from more than 12 million to about 1.5 million between mid-1945 and mid-1947. Defense spending exceeded 41 percent of GDP in 1945. After World War II, the US reduced military spending to 7.2 percent of GDP by 1948. Defense spending doubled from the 1948 low to 15 percent at the height of the Korean War in 1953. Additional sources: https://en.wikipedia.org/wiki/Demobilization_of_United_States_Armed_Forces_after_World_War_II | https://www.americanprogress.org/article/a-historical-perspective-on-military-budgets/ | https://www.stlouisfed.org/on-the-economy/2020/february/war-highest-military-spending-measured | https://www.usgovernmentspending.com/defense_spending_history

94.

Baily, M. N. Pre-1962 drug development costs (baily 1972). Baily (1972) https://samizdathealth.org/wp-content/uploads/2020/12/hlthaff.1.2.6.pdf (1972)

Pre-1962: Average cost per new chemical entity (NCE) was $6.5 million (1980 dollars) Inflation-adjusted to 2024 dollars: $6.5M (1980) ≈ $22.5M (2024), using CPI multiplier of 3.46× Real cost increase (inflation-adjusted): $22.5M (pre-1962) → $2,600M (2024) = 116× increase Note: This represents the most comprehensive academic estimate of pre-1962 drug development costs based on empirical industry data Additional sources: https://samizdathealth.org/wp-content/uploads/2020/12/hlthaff.1.2.6.pdf

95.

Numbers, T. by. Pre-1962 physician-led clinical trials. Think by Numbers: How Many Lives Does FDA Save? https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ (1966)

Pre-1962: Physicians could report real-world evidence directly 1962 Drug Amendments replaced "premarket notification" with "premarket approval", requiring extensive efficacy testing Impact: New regulatory clampdown reduced new treatment production by 70%; lifespan growth declined from 4 years/decade to 2 years/decade Drug Efficacy Study Implementation (DESI): NAS/NRC evaluated 3,400+ drugs approved 1938-1962 for safety only; reviewed >3,000 products, >16,000 therapeutic claims FDA has had authority to accept real-world evidence since 1962, clarified by 21st Century Cures Act (2016) Note: Specific "144,000 physicians" figure not verified in sources Additional sources: https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ | https://www.fda.gov/drugs/enforcement-activities-fda/drug-efficacy-study-implementation-desi | http://www.nasonline.org/about-nas/history/archives/collections/des-1966-1969-1.html

96.

GAO. 95% of diseases have 0 FDA-approved treatments. GAO https://www.gao.gov/products/gao-25-106774 (2025)

95% of diseases have no treatment Additional sources: https://www.gao.gov/products/gao-25-106774 | https://globalgenes.org/rare-disease-facts/

97.

Oren Cass, M. I. RECOVERY trial cost per patient. Oren Cass https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs (2023)

The RECOVERY trial, for example, cost only about $500 per patient... By contrast, the median per-patient cost of a pivotal trial for a new therapeutic is around$41,000. Additional sources: https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs

98.

al., N. E. Á. et. RECOVERY trial global lives saved ( 1 million). NHS England: 1 Million Lives Saved https://www.england.nhs.uk/2021/03/covid-treatment-developed-in-the-nhs-saves-a-million-lives/ (2021)

Dexamethasone saved 1 million lives worldwide (NHS England estimate, March 2021, 9 months after discovery). UK alone: 22,000 lives saved. Methodology: Águas et al. Nature Communications 2021 estimated 650,000 lives (range: 240,000-1,400,000) for July-December 2020 alone, based on RECOVERY trial mortality reductions (36% for ventilated, 18% for oxygen-only patients) applied to global COVID hospitalizations. June 2020 announcement: Dexamethasone reduced deaths by up to 1/3 (ventilated patients), 1/5 (oxygen patients). Impact immediate: Adopted into standard care globally within hours of announcement. Additional sources: https://www.england.nhs.uk/2021/03/covid-treatment-developed-in-the-nhs-saves-a-million-lives/ | https://www.nature.com/articles/s41467-021-21134-2 | https://pharmaceutical-journal.com/article/news/steroid-has-saved-the-lives-of-one-million-covid-19-patients-worldwide-figures-show | https://www.recoverytrial.net/news/recovery-trial-celebrates-two-year-anniversary-of-life-saving-dexamethasone-result

99.

Museum, N. S. 11. M. &. September 11 attack facts. (2024)

2,977 people were killed in the September 11, 2001 attacks: 2,753 at the World Trade Center, 184 at the Pentagon, and 40 passengers and crew on United Flight 93 in Shanksville, Pennsylvania.

100.

Bank, W. World bank singapore economic data. World Bank https://data.worldbank.org/country/singapore (2024)

Singapore GDP per capita (2023): $82,000 - among highest in the world Government spending: 15% of GDP (vs US 38%) Life expectancy: 84.1 years (vs US 77.5 years) Singapore demonstrates that low government spending can coexist with excellent outcomes Additional sources: https://data.worldbank.org/country/singapore

101.

Fund, I. M. IMF singapore government spending data. (2024)

Singapore government spending is approximately 15% of GDP This is 23 percentage points lower than the United States (38%) Despite lower spending, Singapore achieves excellent outcomes: - Life expectancy: 84.1 years (vs US 77.5) - Low crime, world-class infrastructure, AAA credit rating Additional sources: https://www.imf.org/en/Countries/SGP

102.

Organization, W. H. WHO life expectancy data by country. (2024)

Life expectancy at birth varies significantly among developed nations: Switzerland: 84.0 years (2023) Singapore: 84.1 years (2023) Japan: 84.3 years (2023) United States: 77.5 years (2023) - 6.5 years below Switzerland, Singapore Global average: 73 years Note: US spends more per capita on healthcare than any other nation, yet achieves lower life expectancy Additional sources: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-life-expectancy-and-healthy-life-expectancy

103.

CSIS. Smallpox eradication ROI. CSIS https://www.csis.org/analysis/smallpox-eradication-model-global-cooperation.

104.

PMC. Contribution of smoking reduction to life expectancy gains. PMC: Benefits Smoking Cessation Longevity https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447499/ (2012)

Population-level: Up to 14% (9% men, 14% women) of total life expectancy gain since 1960 due to tobacco control efforts Individual cessation benefits: Quitting at age 35 adds 6.9-8.5 years (men), 6.1-7.7 years (women) vs continuing smokers By cessation age: Age 25-34 = 10 years gained; age 35-44 = 9 years; age 45-54 = 6 years; age 65 = 2.0 years (men), 3.7 years (women) Cessation before age 40: Reduces death risk by 90% Long-term cessation: 10+ years yields survival comparable to never smokers, averts 10 years of life lost Recent cessation: <3 years averts 5 years of life lost Additional sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447499/ | https://www.cdc.gov/pcd/issues/2012/11_0295.htm | https://www.ajpmonline.org/article/S0749-3797(24 | https://www.nejm.org/doi/full/10.1056/NEJMsa1211128

105.

ICER. Value per QALY (standard economic value). ICER https://icer.org/wp-content/uploads/2024/02/Reference-Case-4.3.25.pdf (2024)

Standard economic value per QALY: $100,000–$150,000. This is the US and global standard willingness-to-pay threshold for interventions that add costs. Dominant interventions (those that save money while improving health) are favorable regardless of this threshold. Additional sources: https://icer.org/wp-content/uploads/2024/02/Reference-Case-4.3.25.pdf

106.

GAO. Annual cost of u.s. Sugar subsidies. GAO: Sugar Program https://www.gao.gov/products/gao-24-106144

Consumer costs: $2.5-3.5 billion per year (GAO estimate) Net economic cost: $1 billion per year 2022: US consumers paid 2X world price for sugar Program costs $3-4 billion/year but no federal budget impact (costs passed directly to consumers via higher prices) Employment impact: 10,000-20,000 manufacturing jobs lost annually in sugar-reliant industries (confectionery, etc.) Multiple studies confirm: Sweetener Users Association ($2.9-3.5B), AEI ($2.4B consumer cost), Beghin & Elobeid ($2.9-3.5B consumer surplus) Additional sources: https://www.gao.gov/products/gao-24-106144 | https://www.heritage.org/agriculture/report/the-us-sugar-program-bad-consumers-bad-agriculture-and-bad-america | https://www.aei.org/articles/the-u-s-spends-4-billion-a-year-subsidizing-stalinist-style-domestic-sugar-production/

107.

Bank, W. Swiss military budget as percentage of GDP. World Bank: Military Expenditure https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS?locations=CH

2023: 0.70272% of GDP (World Bank) 2024: CHF 5.95 billion official military spending When including militia system costs: 1% GDP (CHF 8.75B) Comparison: Near bottom in Europe; only Ireland, Malta, Moldova spend less (excluding microstates with no armies) Additional sources: https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS?locations=CH | https://www.avenir-suisse.ch/en/blog-defence-spending-switzerland-is-in-better-shape-than-it-seems/ | https://tradingeconomics.com/switzerland/military-expenditure-percent-of-gdp-wb-data.html

108.

Bank, W. Switzerland vs. US GDP per capita comparison. World Bank: Switzerland GDP Per Capita https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=CH

2024 GDP per capita (PPP-adjusted): Switzerland $93,819 vs United States $75,492 Switzerland’s GDP per capita 24% higher than US when adjusted for purchasing power parity Nominal 2024: Switzerland $103,670 vs US $85,810 Additional sources: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=CH | https://tradingeconomics.com/switzerland/gdp-per-capita-ppp | https://www.theglobaleconomy.com/USA/gdp_per_capita_ppp/

109.

Economic Co-operation, O. for & Development. OECD government spending as percentage of GDP. (2024)

OECD government spending data shows significant variation among developed nations: United States: 38.0% of GDP (2023) Switzerland: 35.0% of GDP - 3 percentage points lower than US Singapore: 15.0% of GDP - 23 percentage points lower than US (per IMF data) OECD average: approximately 40% of GDP Additional sources: https://data.oecd.org/gga/general-government-spending.htm

110.

Economic Co-operation, O. for & Development. OECD median household income comparison. (2024)

Median household disposable income varies significantly across OECD nations: United States: $77,500 (2023) Switzerland: $55,000 PPP-adjusted (lower nominal but comparable purchasing power) Singapore: $75,000 PPP-adjusted Additional sources: https://data.oecd.org/hha/household-disposable-income.htm

111.

Institute, C. Chance of dying from terrorism statistic. Cato Institute: Terrorism and Immigration Risk Analysis https://www.cato.org/policy-analysis/terrorism-immigration-risk-analysis

Chance of American dying in foreign-born terrorist attack: 1 in 3.6 million per year (1975-2015) Including 9/11 deaths; annual murder rate is 253x higher than terrorism death rate More likely to die from lightning strike than foreign terrorism Note: Comprehensive 41-year study shows terrorism risk is extremely low compared to everyday dangers Additional sources: https://www.cato.org/policy-analysis/terrorism-immigration-risk-analysis | https://www.nbcnews.com/news/us-news/you-re-more-likely-die-choking-be-killed-foreign-terrorists-n715141

112.

Wikipedia. Thalidomide scandal: Worldwide cases and mortality. Wikipedia https://en.wikipedia.org/wiki/Thalidomide_scandal

The total number of embryos affected by the use of thalidomide during pregnancy is estimated at 10,000, of whom about 40% died around the time of birth. More than 10,000 children in 46 countries were born with deformities such as phocomelia. Additional sources: https://en.wikipedia.org/wiki/Thalidomide_scandal

113.

One, P. Health and quality of life of thalidomide survivors as they age. PLOS One https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210222 (2019)

Study of thalidomide survivors documenting ongoing disability impacts, quality of life, and long-term health outcomes. Survivors (now in their 60s) continue to experience significant disability from limb deformities, organ damage, and other effects. Additional sources: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210222

114.

Bureau, U. C. Historical world population estimates. US Census Bureau https://www.census.gov/data/tables/time-series/demo/international-programs/historical-est-worldpop.html

US Census Bureau historical estimates of world population by country and region (1950-2050). US population in 1960: 180 million of 3 billion worldwide (6%). Additional sources: https://www.census.gov/data/tables/time-series/demo/international-programs/historical-est-worldpop.html

115.

NCBI, F. S. via. Trial costs, FDA study. FDA Study via NCBI https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248200/

Overall, the 138 clinical trials had an estimated median (IQR) cost of $19.0 million ($12.2 million-$33.1 million)... The clinical trials cost a median (IQR) of$41,117 ($31,802-$82,362) per patient. Additional sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248200/

116.

GBD 2019 Diseases and Injuries Collaborators. Global burden of disease study 2019: Disability weights. The Lancet 396, 1204–1222 (2020)

Disability weights for 235 health states used in Global Burden of Disease calculations. Weights range from 0 (perfect health) to 1 (death equivalent). Chronic conditions like diabetes (0.05-0.35), COPD (0.04-0.41), depression (0.15-0.66), and cardiovascular disease (0.04-0.57) show substantial variation by severity. Treatment typically reduces disability weights by 50-80 percent for manageable chronic conditions.

117.

WHO. Annual global economic burden of alzheimer’s and other dementias. WHO: Dementia Fact Sheet https://www.who.int/news-room/fact-sheets/detail/dementia (2019)

Global cost: $1.3 trillion (2019 WHO-commissioned study) 50% from informal caregivers (family/friends, 5 hrs/day) 74% of costs in high-income countries despite 61% of patients in LMICs $818B (2010) → $1T (2018) → $1.3T (2019) - rapid growth Note: Costs increased 35% from 2010-2015 alone. Informal care represents massive hidden economic burden Additional sources: https://www.who.int/news-room/fact-sheets/detail/dementia | https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.12901

118.

Oncology, J. Annual global economic burden of cancer. JAMA Oncology: Global Cost 2020-2050 https://jamanetwork.com/journals/jamaoncology/fullarticle/2801798 (2020)

2020-2050 projection: $25.2 trillion total ($840B/year average) 2010 annual cost: $1.16 trillion (direct costs only) Recent estimate: $3 trillion/year (all costs included) Top 5 cancers: lung (15.4%), colon/rectum (10.9%), breast (7.7%), liver (6.5%), leukemia (6.3%) Note: China/US account for 45% of global burden; 75% of deaths in LMICs but only 50.0% of economic cost Additional sources: https://jamanetwork.com/journals/jamaoncology/fullarticle/2801798 | https://www.nature.com/articles/d41586-023-00634-9

119.

CDC. U.s. Chronic disease healthcare spending. CDC https://www.cdc.gov/chronic-disease/data-research/facts-stats/index.html

Chronic diseases account for 90% of U.S. healthcare spending ( $3.7T/year). Additional sources: https://www.cdc.gov/chronic-disease/data-research/facts-stats/index.html

120.

Care, D. Annual global economic burden of diabetes. Diabetes Care: Global Economic Burden https://diabetesjournals.org/care/article/41/5/963/36522/Global-Economic-Burden-of-Diabetes-in-Adults

2015: $1.3 trillion (1.8% of global GDP) 2030 projections: $2.1T-2.5T depending on scenario IDF health expenditure: $760B (2019) → $845B (2045 projected) 2/3 direct medical costs ($857B), 1/3 indirect costs (lost productivity) Note: Costs growing rapidly; expected to exceed $2T by 2030 Additional sources: https://diabetesjournals.org/care/article/41/5/963/36522/Global-Economic-Burden-of-Diabetes-in-Adults | https://www.thelancet.com/journals/landia/article/PIIS2213-8587(17

121.

World Bank, B. of E. A. US GDP 2024 ($28.78 trillion). World Bank https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=US (2024)

US GDP reached $28.78 trillion in 2024, representing approximately 26% of global GDP. Additional sources: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=US | https://www.bea.gov/news/2024/gross-domestic-product-fourth-quarter-and-year-2024-advance-estimate

122.

Cardiology, I. J. of. Annual global economic burden of heart disease. Int’l Journal of Cardiology: Global Heart Failure Burden02238-9/abstract) https://www.internationaljournalofcardiology.com/article/S0167-5273(13 (2050)

Heart failure alone: $108 billion/year (2012 global analysis, 197 countries) US CVD: $555B (2016) → projected $1.8T by 2050 LMICs total CVD loss: $3.7T cumulative (2011-2015, 5-year period) CVD is costliest disease category in most developed nations Note: No single $2.1T global figure found; estimates vary widely by scope and year Additional sources: https://www.internationaljournalofcardiology.com/article/S0167-5273(13 | https://www.ahajournals.org/doi/10.1161/CIR.0000000000001258

123.

CSV, S. U. L. E. F. B. 1543-2019. US life expectancy growth 1880-1960: 3.82 years per decade. (2019)

Pre-1962: 3.82 years/decade Post-1962: 1.54 years/decade Reduction: 60% decline in life expectancy growth rate Additional sources: https://ourworldindata.org/life-expectancy | https://www.mortality.org/ | https://www.cdc.gov/nchs/nvss/mortality_tables.htm

124.

CSV, S. U. L. E. F. B. 1543-2019. Post-1962 slowdown in life expectancy gains. (2019)

Pre-1962 (1880-1960): 3.82 years/decade Post-1962 (1962-2019): 1.54 years/decade Reduction: 60% decline Temporal correlation: Slowdown occurred immediately after 1962 Kefauver-Harris Amendment See detailed calculation: [life-expectancy-increase-pre-1962](#life-expectancy-increase-pre-1962) Additional sources: https://ourworldindata.org/life-expectancy | https://www.mortality.org/ | https://www.cdc.gov/nchs/nvss/mortality_tables.htm

125.

Disease Control, C. for & Prevention. US life expectancy 2023. (2024)

US life expectancy at birth was 77.5 years in 2023 Male life expectancy: 74.8 years Female life expectancy: 80.2 years This is 6-7 years lower than peer developed nations despite higher healthcare spending Additional sources: https://www.cdc.gov/nchs/fastats/life-expectancy.htm

126.

Bureau, U. C. US median household income 2023. (2024)

US median household income was $77,500 in 2023 Real median household income declined 0.8% from 2022 Gini index: 0.467 (income inequality measure) Additional sources: https://www.census.gov/library/publications/2024/demo/p60-282.html

127.

Statista. US military budget as percentage of GDP. Statista https://www.statista.com/statistics/262742/countries-with-the-highest-military-spending/ (2024)

U.S. military spending amounted to 3.5% of GDP in 2024. In 2024, the U.S. spent nearly $1 trillion on its military budget, equal to 3.4% of GDP. Additional sources: https://www.statista.com/statistics/262742/countries-with-the-highest-military-spending/ | https://www.sipri.org/sites/default/files/2025-04/2504_fs_milex_2024.pdf

128.

Bureau, U. C. Number of registered or eligible voters in the u.s. US Census Bureau https://www.census.gov/newsroom/press-releases/2025/2024-presidential-election-voting-registration-tables.html (2024)

73.6% (or 174 million people) of the citizen voting-age population was registered to vote in 2024 (Census Bureau). More than 211 million citizens were active registered voters (86.6% of citizen voting age population) according to the Election Assistance Commission. Additional sources: https://www.census.gov/newsroom/press-releases/2025/2024-presidential-election-voting-registration-tables.html | https://www.eac.gov/news/2025/06/30/us-election-assistance-commission-releases-2024-election-administration-and-voting

129.

Senate, U. S. Treaties. U.S. Senate https://www.senate.gov/about/powers-procedures/treaties.htm

The Constitution provides that the president ’shall have Power, by and with the Advice and Consent of the Senate, to make Treaties, provided two-thirds of the Senators present concur’ (Article II, section 2). Treaties are formal agreements with foreign nations that require two-thirds Senate approval. 67 senators (two-thirds of 100) must vote to ratify a treaty for it to take effect. Additional sources: https://www.senate.gov/about/powers-procedures/treaties.htm

130.

Commission, F. E. Statistical summary of 24-month campaign activity of the 2023-2024 election cycle. (2023)

Presidential candidates raised $2 billion; House and Senate candidates raised $3.8 billion and spent $3.7 billion; PACs raised $15.7 billion and spent $15.5 billion. Total federal campaign spending approximately $20 billion. Additional sources: https://www.fec.gov/updates/statistical-summary-of-24-month-campaign-activity-of-the-2023-2024-election-cycle/

131.

OpenSecrets. Federal lobbying hit record $4.4 billion in 2024. (2024)

Total federal lobbying reached record $4.4 billion in 2024. The $150 million increase in lobbying continues an upward trend that began in 2016. Additional sources: https://www.opensecrets.org/news/2025/02/federal-lobbying-set-new-record-in-2024/

132.

Kirk (2011), H. &. Valley of death in drug development. (2011)

The overall failure rate of drugs that passed into Phase 1 trials to final approval is 90%. This lack of translation from promising preclinical findings to success in human trials is known as the "valley of death." Estimated 30-50% of promising compounds never proceed to Phase 2/3 trials primarily due to funding barriers rather than scientific failure. The late-stage attrition rate for oncology drugs is as high as 70% in Phase II and 59% in Phase III trials.

133.

DOT. DOT value of statistical life ($13.6M). DOT: VSL Guidance 2024 https://www.transportation.gov/office-policy/transportation-policy/revised-departmental-guidance-on-valuation-of-a-statistical-life-in-economic-analysis (2024)

Current VSL (2024): $13.7 million (updated from $13.6M) Used in cost-benefit analyses for transportation regulations and infrastructure Methodology updated in 2013 guidance, adjusted annually for inflation and real income VSL represents aggregate willingness to pay for safety improvements that reduce fatalities by one Note: DOT has published VSL guidance periodically since 1993. Current $13.7M reflects 2024 inflation/income adjustments Additional sources: https://www.transportation.gov/office-policy/transportation-policy/revised-departmental-guidance-on-valuation-of-a-statistical-life-in-economic-analysis | https://www.transportation.gov/regulations/economic-values-used-in-analysis

134.

ONE, P. Cost per DALY for vitamin a supplementation. PLOS ONE: Cost-effectiveness of "Golden Mustard" for Treating Vitamin A Deficiency in India (2010) https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012046 (2010)

India: $23-$50 per DALY averted (least costly intervention, $1,000-$6,100 per death averted) Sub-Saharan Africa (2022): $220-$860 per DALY (Burkina Faso: $220, Kenya: $550, Nigeria: $860) WHO estimates for Africa: $40 per DALY for fortification, $255 for supplementation Uganda fortification: $18-$82 per DALY (oil: $18, sugar: $82) Note: Wide variation reflects differences in baseline VAD prevalence, coverage levels, and whether intervention is supplementation or fortification Additional sources: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012046 | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0266495

135.

News, U. Clean water & sanitation (LMICs) ROI. UN News https://news.un.org/en/story/2014/11/484032 (2014).

136.

PMC. Cost-effectiveness threshold ($50,000/QALY). PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC5193154/

The $50,000/QALY threshold is widely used in US health economics literature, originating from dialysis cost benchmarks in the 1980s. In US cost-utility analyses, 77.5% of authors use either $50,000 or $100,000 per QALY as reference points. Most successful health programs cost $3,000-10,000 per QALY. WHO-CHOICE uses GDP per capita multiples (1× GDP/capita = "very cost-effective", 3× GDP/capita = "cost-effective"), which for the US ( $70,000 GDP/capita) translates to $70,000-$210,000/QALY thresholds. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC5193154/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC9278384/

137.

Institute, I. B. Chronic illness workforce productivity loss. Integrated Benefits Institute 2024 https://www.ibiweb.org/resources/chronic-conditions-in-the-us-workforce-prevalence-trends-and-productivity-impacts (2024)

78.4% of U.S. employees have at least one chronic condition (7% increase since 2021) 58% of employees report physical chronic health conditions 28% of all employees experience productivity loss due to chronic conditions Average productivity loss: $4,798 per employee per year Employees with 3+ chronic conditions miss 7.8 days annually vs 2.2 days for those without Note: 28% productivity loss translates to roughly 11 hours per week (28% of 40-hour workweek) Additional sources: https://www.ibiweb.org/resources/chronic-conditions-in-the-us-workforce-prevalence-trends-and-productivity-impacts | https://www.onemedical.com/mediacenter/study-finds-more-than-half-of-employees-are-living-with-chronic-conditions-including-1-in-3-gen-z-and-millennial-employees/ | https://debeaumont.org/news/2025/poll-the-toll-of-chronic-health-conditions-on-employees-and-workplaces/

138.

Rare Diseases (2024), O. J. of. Rare disease treatment gap. Orphanet Journal of Rare Diseases (2024) https://ojrd.biomedcentral.com/articles/10.1186/s13023-024-03398-1 (2024)

Most patients wait 5 to 10 years to get an accurate diagnosis - and only about 5% of rare diseases have an FDA-approved treatment. Over the 40 years of the ODA, 6,340 orphan drug designations were granted, representing drug development for 1,079 rare diseases out of 7,000-10,000 known rare conditions.

139.

Orphanet, C. estimate based on. Average time to cure under current system.

Queue-based calculation: 7,000 diseases without effective treatment ÷ 15 diseases getting first treatment per year = 467 years for the average disease to receive a cure under the status quo system. This is consistent with the fact that only 5% of rare diseases have treatments after 40+ years of the Orphan Drug Act. Well-funded diseases may take 30-50 years; underfunded diseases 100-500+ years; and neglected diseases effectively never within human planning horizons.

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Combination Therapy Space: 45.1B combinations

Sensitivity Analysis

Pairwise Drug Combinations: 45.1M combinations

Sensitivity Analysis

Combination Therapy Exploration Time (Current): 13.7M years

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Known Safe Exploration Time (Current): 2.88k years

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Current Patient Participation Rate in Clinical Trials: 0.0792%

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Total Annual Decentralized Framework for Drug Assessment Operational Costs: $40M

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings: $58.6B

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

dFDA Direct Funding Cost per DALY: $0.841

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

dFDA Direct Funding NPV (Exploration Period): $475B

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Total DALYs Lost from Disease Eradication Delay: 7.94B DALYs

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Total Deaths from Disease Eradication Delay: 416M deaths

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Total Economic Loss from Disease Eradication Delay: $1.19 quadrillion

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Years Lived with Disability During Disease Eradication Delay: 873M years

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Years of Life Lost from Disease Eradication Delay: 7.07B years

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

dFDA New Treatments Per Year: 185 diseases/year

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only): $58.6B

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Total NPV Annual OPEX: $40M

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted): $389B

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

NPV Net Benefit (R&D Only): $389B

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years: $342M

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Total NPV Cost: $611M