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Hanson’s Strategy for Saving Life-Years

Summary:
My knowledge of epidemiology is mediocre at best, but health economist Robin Hanson is the smartest person I know.  Even when he’s totally wrong, he improves our thinking.  And even when he seems totally wrong, he is often right.  In the face of great outrage, Robin here suggested that deliberately exposing some people to the coronavirus could save lives.  Then he produced some simulations to confirm his intuition.  In his latest post, he refines his simulation to show that under his assumptions, deliberately exposing the young has enormous health benefits: I’ve changed my prior spreadsheet model so that the population is split into two groups who differ in their death rate, with the deliberately exposed taken only from one of those groups.

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My knowledge of epidemiology is mediocre at best, but health economist Robin Hanson is the smartest person I know.  Even when he’s totally wrong, he improves our thinking.  And even when he seems totally wrong, he is often right.  In the face of great outrage, Robin here suggested that deliberately exposing some people to the coronavirus could save lives.  Then he produced some simulations to confirm his intuition.  In his latest post, he refines his simulation to show that under his assumptions, deliberately exposing the young has enormous health benefits:

I’ve changed my prior spreadsheet model so that the population is split into two groups who differ in their death rate, with the deliberately exposed taken only from one of those groups. Combining estimated COVID19 death rates by age with US population stats, I find that about half of the population is over 40, and has a COVID19 death rate about 23 times larger than the under 40 half. 

So I compared four options regarding who is deliberately exposed: no one (baseline), random folks, folks 40 and over, or folks under 40. I assumed that just enough are exposed per day to fill a quarantine that holds 10M, and I varied the number of days of deliberate exposure within each option to min deaths in that option. (I also assume that higher death rates induce higher needed ICU days.)

Below I show graphs of contagious, deaths, and dead % of sick for the first 250 days of each scenario. In the baseline 14.3M die, while if random folks are exposed for 31 days, 11.2M die. If only the old are exposed for 15 days, 12.9M die, while if only the young are exposed for 54 days 8.1M die. 

As the death of a young person is more of a tragedy due to their having more years left to live, we might want to adjust for this remaining-life-years-destroyed effect. Doing so, I find that when only the old are exposed for 15 days, there are 4.22M adjusted deaths, while when only the young are exposed for 54 days there are 2.78M adjusted deaths. That’s only 20% of the deaths in the baseline, and I have not yet searched for an optimal age cutoff, optimal cutoffs using gender and co-morbidities, an optimal schedule of exposures per day, or an optimal quarantine capacity!

Thus exposing the young seems better than exposing the old, which are both much better than exposing random people or no one. As I’ve said, this process rewards variance!

I urge people who know more than I do to ponder Robin’s thoughts closely and calmly.

Bryan Caplan
Bryan Caplan is Professor of Economics at George Mason University and Senior Scholar at the Mercatus Center. He has published in the New York Times, the Washington Post, the Wall Street Journal, the American Economic Review, the Economic Journal, the Journal of Law and Economics, and Intelligence, and has appeared on 20/20, FoxNews, and C-SPAN. Bryan Caplan blogs on EconLog.

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