Our sixth COVID white paper is “Why We Must Test Millions a Day,” by Divya Siddarth and E. Glen Weyl.
The appendix to the paper can be found here, and the Python code can be found here.
There is growing consensus from leading think tanks such as the American Enterprise Institute and the Center for American Progress that the way out of lockdown is through a massive testing and tracing infrastructure. Yet there is much less clarity on how large this infrastructure must be to allow a safe return to work. Both the AEI and CAP proposals suggest that hundreds of thousands of tests per day might suffice. However, to date, we are not aware of epidemiological models that attempt to estimate the scale of required testing. This paper tries to fill this gap with rough and preliminary but easily explicable calculations. These suggest that, depending on what tracing technology is used in conjunction with testing, at least millions and possibly hundreds of millions of tests per day will be needed. While we estimate that such capacity is possible by late spring or early summer, we believe that the AEI and CAP timetables and cost estimates are unrealistic and that we must invest much more aggressively if we are to allow a return to work.