Summary
- I Fermi estimate the cost-effectiveness of epidemic/pandemic preparedness is 0.00236 DALY/$.
- Relative to the above, I calculate GiveWell’s top charities are 4.12 times as cost-effective, and corporate campaigns for chicken welfare, such as the ones supported by The Humane League (THL), 6.35 k times.
Calculations
My calculations are in this Sheet.
I Fermi estimate the cost-effectiveness of epidemic/pandemic preparedness of 0.00236 DALY/$ multiplying:
- The expected annual epidemic/pandemic disease burden of 68.2 MDALY. I obtained this from the product between:
- The expected annual epidemic/pandemic deaths of 1.61 M, which I determined multiplying:
The epidemic/pandemic deaths per human-year from 1500 to 2023 of 1.98*10^-4, which is the ratio between 160 M epidemic/pandemic deaths, and 808 G human-years from Marani et. al 2021[1].
- The population predicted for 2024 of 8.12 G.
- The disease burden per death in 2021 of 42.4 DALY.
- The expected annual epidemic/pandemic deaths of 1.61 M, which I determined multiplying:
- The relative reduction of the expected annual epidemic/pandemic disease burden per annual cost of 3.46 %/G$. I got this aggregating the following estimates with the geometric mean:
- 8 %/G$ (= 0.2/(250*10^9/100)), which is based on Millett & Snyder-Beattie 2017:
- “We extend the World Bank's assumptions to include bioterrorism and biowarfare—that is, we assume that the healthcare infrastructure would reduce bioterrorism and biowarfare fatalities by 20%”.
- “We calculate that purchasing 1 century's worth of global protection in this form would cost on the order of $250 billion, assuming that subsequent maintenance costs are lower but that the entire system needs intermittent upgrading”.
- 1.5 %/G$ (= 0.3/(20*10^9)), which is based on Bernstein et. al 2022:
- 30 % is the mean between 10 % and 50 %, which are the values studied in Table 2.
- “We find that the sum of our median cost estimates of primary prevention (~$20 billion) are ~1/20 of the low-end annualized value of lives lost to emerging viral zoonoses and <1/10 of the annualized economic losses”.
- 8 %/G$ (= 0.2/(250*10^9/100)), which is based on Millett & Snyder-Beattie 2017:
Relative to epidemic/pandemic preparedness, I calculate:
- GiveWell’s top charities are 4.21 (= 0.00994/0.00236) times as cost-effective.
- Corporate campaigns for chicken welfare, such as the ones supported by THL, are 6.35 k (= 15.0/0.00236) times as cost-effective.
- ^ 1 G stands for 1 billion. I assumed 5 k deaths (= (0 + 10)/2*10^3) for epidemics/pandemics qualitatively inferred (said) to have caused less than 10 k deaths, which are coded as having caused -999 (0) deaths. I also considered the deaths from COVID-19, which is not in the original dataset.
These kinds of analyses are generally a waste of time, because the people performing them have no idea about how outbreaks are identified and controlled. They have good intentions, but outbreak control isn't a simple linear world where you know all the variables and you can work with averages. As a result, these estimates tend to have little basis in reality.
Take the numbers from Berstein et al - they're patently ridiculous! "$19 billion to close down China’s wildlife farming industry". Never mind the credibility of the $19bn figure... who's going to tell China to shut down anything? Who think's the CCP are just going to do what they're told? What kind of a plan is this??
If you want to do a cost/benefit analysis, you need to do it by strategy. And there are lots of different strategies.
For example, what's the cost/benefit of a rapid elimination strategy? What's the cost/benefit on having wastewater / sewerage / environmental / random testing in major international ports of entry? What's the cost/benefit of investing in rapid testing manufacturing capacity? Or of training HCWs to implement the strategy?
If you respond quickly, you can reduce the size of the problem by orders of magnitude, and therefore reduce the costs of resolving it by orders of magnitude too. So where does that appear in the analysis? Nowhere, because these kinds of analyses don't allow for it. Instead, they just make vague assumptions about reducing healthcare costs, which is totally unsatisfactory.
I've tried to explain this before...
https://forum.effectivealtruism.org/posts/utE4WqYjjmYDwoiuJ/pandemicriskman-s-quick-takes#u2JxaKrmfJF4hbfh5
Thanks for the comment!
I took Millett & Snyder-Beattie 2017's and Bernstein et. al 2022's numbers at face value, but they are far from rigorous estimates, and I would agree better modelling is needed.
Sidenote. I would not be surprised if their numbers are very off, but I think it is better to avoid terms like "ridiculous", which are confrontational, and therefore can make thinking clearly more difficult.