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Is This Crazy?

Keywords

war-on-disease, 1-percent-treaty, medical-research, public-health, peace-dividend, decentralized-trials, dfda, dih, victory-bonds, health-economics, cost-benefit-analysis, clinical-trials, drug-development, regulatory-reform, military-spending, peace-economics, decentralized-governance, wishocracy, blockchain-governance, impact-investing

ImportantWhy You Should Take 15 Minutes to Validate This Model

If this model is right and these policies are adopted, you will have helped to save 10.7 billion deaths (95% CI: 7.39 billion deaths-16.2 billion deaths) and prevent 565 billion DALYs (95% CI: 361 billion DALYs-877 billion DALYs) of unnecessary suffering. (Which would be nice.)

6.65 thousand diseases (95% CI: 5.70 thousand diseases-8.24 thousand diseases) have no effective treatment. At the current discovery rate of 15 diseases/year (95% CI: 8 diseases/year-30 diseases/year), clearing that backlog takes 443 years (95% CI: 324 years-712 years). We’re proposing a treaty that redirects 1% of global military spending to pragmatic clinical trials, cutting the backlog to 36 years (95% CI: 11.6 years-77.2 years) and getting treatments to patients an average of 212 years (95% CI: 135 years-355 years) sooner.

The insanely long full paper and really boring calculations and uncertainty analysis page contain complete derivations. Please flag any assumption you find implausible, any calculation step that needs revision, or any framing that would not survive peer review. Any feedback to [email protected] would be greatly appreciated! :D

Bottom-Line Claims

Claim Value
Cost per DALY (treaty campaign)

$0.0018 (95% CI: $0.0007-$0.0041)

Cost per DALY (risk-adjusted at 1% (95% CI: 0.1%-10%) success)

$0.177 (95% CI: $0.029-$3.20)

Cost per DALY (direct funding alternative)

$0.841 (95% CI: $0.242-$1.75)

Total DALYs averted

565 billion DALYs (95% CI: 361 billion DALYs-877 billion DALYs)

Lives saved

10.7 billion deaths (95% CI: 7.39 billion deaths-16.2 billion deaths)

ROI (complete benefits)

84.8M:1 (95% CI: 46.6M:1-144M:1)

Treaty leverage vs. direct funding

475x (95% CI: 329x-462x)

Bed net benchmark $89 (95% CI: $78-$100)/DALY

All values include 95% confidence intervals from Monte Carlo simulation (10,000 draws). Hover/click any value for the full range.

The Treaty Mechanism

Component Value Source
Global military spending $2.72T/year SIPRI 2024
Treaty redirect

1%

Policy parameter
Annual funding unlocked

$27.2B

= military x 1%
Bond payouts (10%) $2.72B/year Incentive Alignment Bonds
Political allocation (10%) $2.72B/year Campaign infrastructure
Net R&D budget $21.8B = $27.2B - $2.72B - $2.72B
Campaign cost (one-time)

$1B

$250M viral + $650M lobbying + $100M reserve
Annual benefits (peace + R&D)

$172B (95% CI: $140B-$213B)

Peace dividend + trial cost savings

Calculation Chain

The logic builds in six steps.

Step 1: The Backlog

Input Value Source
Diseases without effective treatment

6.65 thousand diseases (95% CI: 5.70 thousand diseases-8.24 thousand diseases)

Orphanet 2024 (95% of ~7,000 rare diseases)
Current discovery rate

15 diseases/year (95% CI: 8 diseases/year-30 diseases/year)

FDA + ODA historical data
Backlog clearance time

443 years (95% CI: 324 years-712 years)

= 6,650 / 15

Step 2: Trial Capacity Multiplier

Input Value Source
Pragmatic trial cost/patient

$929 (95% CI: $97-$3K)

10x conservative vs. $97 meta-analysis median (Ramsberg & Platt 2018, 108 trials)
Traditional Phase III cost/patient

$41K (95% CI: $20K-$120K)

FDA study median
Annual R&D budget

$21.8B

From treaty mechanism above
Fundable patients/year

23.4 million patients/year (95% CI: 9.44 million patients/year-96.8 million patients/year)

= budget / cost per patient
Current trial slots

1.90 million patients/year (95% CI: 1.50 million patients/year-2.30 million patients/year)

Global enrollment
Capacity multiplier 12.3x (95% CI: 4.19x-61.3x) = fundable / current slots
Accelerated clearance time

36 years (95% CI: 11.6 years-77.2 years)

= 443 / 12.3

Step 3: Timeline Shift

Component Value Derivation
Discovery acceleration

204 years (95% CI: 123 years-350 years)

= (443/2) x (1 - 1/12.3)
Efficacy lag elimination

8.2 years (95% CI: 4.85 years-11.5 years)

Post-Phase I access removes Phase II/III delay
Total timeline shift 212 years (95% CI: 135 years-355 years) = discovery + efficacy lag

Step 4: Health Impact

Input Value Source
Global annual DALY burden

2.88 billion DALYs/year (95% CI: 2.63 billion DALYs/year-3.13 billion DALYs/year)

WHO/IHME GBD 2021
Eventually avoidable fraction

92.6% (95% CI: 50%-98%)

Conservative estimate (see below)
Timeline shift

212 years (95% CI: 135 years-355 years)

Step 3
DALYs averted 565 billion DALYs (95% CI: 361 billion DALYs-877 billion DALYs) = burden x avoidable% x shift

Step 5: Cost-Effectiveness (Two Framings)

Framing Cost DALYs Cost/DALY
Treaty campaign

$1B

565 billion DALYs (95% CI: 361 billion DALYs-877 billion DALYs)

$0.0018 (95% CI: $0.0007-$0.0041)

Direct funding (NPV at 3%)

$475B (95% CI: $211B-$651B)

565 billion DALYs (95% CI: 361 billion DALYs-877 billion DALYs)

$0.841 (95% CI: $0.242-$1.75)

Note: Costs are discounted at 3% social discount rate; DALYs are undiscounted (standard in GBD methodology).

Step 6: Risk Adjustment

Input Value Derivation
Political success probability

1% (95% CI: 0.1%-10%)

Conservative (99% failure assumed)
Risk-adjusted cost/DALY

$0.177 (95% CI: $0.029-$3.20)

= $0.0018 / 1%
Risk-adjusted vs. bed nets 503x (95% CI: 29.9x-3.0kx) better Still superior at 1% success

Key Assumptions Requiring Sign-Off

Warning1. Eventually Avoidable Fraction: 92.6% (95% CI: 50%-98%) of disease deaths

Single most influential assumption. Remaining ~7.4% covers accidents, violence, and fundamentally irreducible causes.

Our defense: 99.7% (95% CI: 99.5%-99.8%) of the therapeutic search space remains untested (9.50 thousand compounds (95% CI: 7.00 thousand compounds-12.0 thousand compounds) safe compounds x 1.00 thousand diseases (95% CI: 800 diseases-1.20 thousand diseases) = 9.50 million combinations pairings, only 0.342% (95% CI: 0.21%-0.514%) tested). 30% of approved drugs gain new indications, demonstrating effective treatments exist but haven’t been matched to conditions. Emerging modalities (mRNA platforms, CRISPR gene therapy, epigenetic reprogramming, AI-guided drug discovery) are expanding the space of testable candidates faster than ever. At 50%, all headline numbers halve but cost-effectiveness remains strong. Full defense.

Question: Is 92.6% (95% CI: 50%-98%) defensible, or what figure would you use?

Warning2. Trial Capacity = Discovery Rate (Linear Assumption)

A 12.3x (95% CI: 4.19x-61.3x) increase in trial slots produces a proportional increase in the discovery rate (15 diseases/year (95% CI: 8 diseases/year-30 diseases/year) -> 185 diseases/year (95% CI: 107 diseases/year-490 diseases/year)).

Our defense: Linear is conservative. 40% (95% CI: 25%-55%) of promising compounds are abandoned at the “valley of death” because Phase II/III costs ($41K (95% CI: $20K-$120K)/patient) make them uneconomical, not because they lack efficacy. Eliminating this cost barrier would reactivate ~20 drugs/year (95% CI: 18 drugs/year-22.6 drugs/year) additional approvals. Knowledge accumulation creates potential for superlinear returns: each trial generates efficacy signals, biomarker insights, and combination data that inform subsequent trials. Pre-1962 decentralized physician-led testing achieved drug development at $24.7M (95% CI: $19.5M-$30M)/drug vs. today’s $2.60B (95% CI: $1.50B-$4B), consistent with trial throughput being the binding constraint.

Question: Should we model diminishing returns, or does the superlinear case justify the linear assumption as a middle ground?

Warning3. Political Success Probability: 1% (95% CI: 0.1%-10%)

Risk-adjusted at 1% (95% CI: 0.1%-10%) (99% failure), cost-effectiveness ($0.177 (95% CI: $0.029-$3.20)/DALY) still beats bed nets ($89 (95% CI: $78-$100)/DALY).

Our defense: The math dominates the politics. Even assuming 99% failure, expected cost-effectiveness ($0.177 (95% CI: $0.029-$3.20)/DALY) remains 503x (95% CI: 29.9x-3.0kx) better than bed nets. The model doesn’t need political optimism to work. Separately, Incentive Alignment Bonds address feasibility by making treaty support financially self-interested for legislators, investors, and military contractors (military lobbyists currently earn $1,813 per $1 spent on influence; IABs offer comparable returns for treaty compliance). Full political economy analysis.

Question: Is 1% (95% CI: 0.1%-10%) the right baseline?

Warning4. $1B Campaign -> $27.2B/yr Government Funding (Treaty Leverage)

475x (95% CI: 329x-462x) leverage assumes $1B advocacy campaign redirects 1% of global military spending.

Our defense: We present both framings. Direct funding ($0.841 (95% CI: $0.242-$1.75)/DALY) validates the pragmatic trial model independent of political feasibility. Treaty framing ($0.0018 (95% CI: $0.0007-$0.0041)/DALY) reflects policy advocacy leverage, which has precedent: Gates Foundation’s $10B vaccine pledge leveraged $200B+ in government commitments (20x); Bloomberg’s $1B in tobacco control unlocked $15B+ in government spending (15x). The 1% Treaty targets a larger base (global military budgets), enabling proportionally higher leverage. Full comparison.

Question: Is the leverage ratio plausible?

Warning5. Pragmatic Trial Cost: $929 (95% CI: $97-$3K)/patient

Deliberately conservative.

Our defense: Ramsberg & Platt (2018) reviewed 108 embedded pragmatic trials across multiple therapeutic areas; of 64 with cost data, median was $97/patient. Our central estimate is 10x higher than the meta-analysis median. RECOVERY achieved $500 (95% CI: $400-$2.50K)/patient across 186 sites. Cost reductions come from structural factors: existing healthcare encounters, EHR-based data capture, broader eligibility. At our upper CI bound ($3,000/patient), the capacity multiplier drops from 12.3x to ~3.8x, still cutting the backlog from 443 years to ~116 years.

Question: Is the meta-analysis median ($97) or our conservative anchor ($929 (95% CI: $97-$3K)) more appropriate?

Note on framing: All health impact figures are cumulative over the acceleration window, not annual. This is inherent to the model: treatments arrive X years earlier than an unknown future discovery date, and the benefit is the disease burden during that gap. Same methodology as smallpox eradication ROI calculations.

Full Materials

We welcome any feedback on the numbers, methodology, or framing. Please send comments to [email protected].