Cost-effectiveness of epidemic/pandemic preparedness
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 disease burden per death in 2021 of 42.4 DALY.
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 (~$20 billion) are ~1/20 of the low-end annualized value of lives lost to emerging viral zoonoses and <1/10 of the annualized economic losses”.
Relative to epidemic/pandemic preparedness, I calculate:
GiveWell’s top charities are 4.21 (= 0.00994/0.00236) times as cost-effective.
Corporate campaigns for chicken welfare, such as the ones supported by THL, are 6.35 k (= 15.0/0.00236) times as cost-effective.
^ 1 G stands for 1 billion. I assumed 5 k deaths (= (0 + 10)/2*10^3) for epidemics/pandemics qualitatively inferred (said) to have caused less than 10 k deaths, which are coded as having caused −999 (0) deaths. I also considered the deaths from COVID-19, which is not in the original dataset.
This seems intuitively in the right ballpark (within an order of magnitude of GiveWell), but I’d caution that, as far as I can tell, the World Bank and Bernstein et al. numbers are basically made up.
I’ve previously written about how to identify higher impact opportunities. In particular, we need to be careful about the counterfactuals here because a lot of the money on pandemic preparedness comes from governments who would otherwise spend on even less cost effective things.
Thanks for the comment, Joshua!
Because we do not know the relative reduction in the expected annual deaths caused by their proposed measures, right? I guess their values are optimistic, such that GiveWell’s top charities are more than 4.12 times as cost-effective.
Interesting analysis! I don’t have any experience conducting such analyses myself, but I was curious which interventions are considered part of pandemic preparedness when calculating the total cost. Does it also include indirect costs, such as research funding or capacity-building projects?
Thanks for the relevant question, Dhruvin!
Below is the relevant section from Bernstein et. al 2022. I have bolded the 6 measures included in the annual cost of 20 G$.
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.
Interesting! This is a very surprising result to me because I am mostly used to hearing about how cost effective pandemic prevention is and this estimate seems to disagree with that.
Shouldn’t this be a relatively major point against prioritizing biorisk as a cause area? (at least w/o taking into account strong long termism and the moral catastrophe of extinction)
Not really. This post’s cost-effectiveness calculation was done at the cause area level, so it’s an average of many interventions of highly varying cost-effectiveness, while GW top charities’ cost-eff are evaluated at the (org-specific) intervention level.
Thanks for the comment, Jacob!
Note that the cost-effectiveness of epidemic/pandemic preparedness I got of 0.00236 DALY/$ is still quite high. The value of a statistical life in high income countries is around 1 to 10 M$, which, for 51 DALY averted per life saved[1], leads to 5.10*10^-6 (= 51/(10*10^6)) to 5.10*10^-5 DALY/$ (= 51/10^6), i.e. 0.216 % (= 5.10*10^-6/0.00236) to 2.16 % (= 5.10*10^-5/0.00236) of my estimate for the cost-effectiveness of epidemic/pandemic preparedness.
Not so much for prioritising global health and development over biorisk, since GiveWell’s top charities being 4.21 times as cost-effective is not much considering uncertainty in my estimates. However, I would say definitely so for prioritising animal welfare over biorisk.
It is unclear to me whether such considerations would lead to prioritising biorisk, even under expected total hedonistic utilitarianism (which I strongly endorse).
According to Open Philanthropy, “GiveWell uses moral weights for child deaths that would be consistent with assuming 51 years of foregone life in the DALY framework (though that is not how they reach the conclusion)”.
Point well-taken.
I appreciate you writing and sharing those posts trying to model and quantify the impact of x-risk work and question the common arguments given for astronomical EV.
I hope to take a look at those more in depth some time and critically assess what I think about them. Honestly, I am very intrigued by engaging with well informed disagreement around the astronomical EV of x-risk focused approaches. I find your perspective here interesting and I think engaging with it might sharpen my own understanding.
:)
Thanks, Jacob! That is nice to know.