How much difference does it make if the test used to send people into quarantine is bad? Not as much as you might think.
The simulated data here contrast policies that isolate people who test positive using four different assumptions about the quality of the test. Even a very bad test cuts the fraction of the population who are ultimately infected almost in half. And when I say bad, I mean bad – an 80% false negative rate, which means that 4 out of 5 of people who are truly infectious will get a negative test result – i.e. a result saying that they are not infectious.
In a previous post, I presented a “dots in a box” model of the spread of a virus. In this post, I use it to compare the economic and social cost of two policies that are equally effective at containing the virus.
What the simulations show is that if we use a test to determine who gets put into isolation the fraction of the population that needs to be confined and isolated is dramatically smaller. These benefits are available even with an imperfect test and without doing any contact tracing. It does take frequent testing, with each person getting retested roughly every two weeks.
To understand the effects that more testing could have on the course of the pandemic, I constructed a simple model that I could use to simulate and visualize the effects of different policies. This post, the first in a series, introduces the model. In a follow up post, I’ll use the model to compare the effects of a policy of nonspecific or uniform social distancing with those from a targeted policy that uses tests to make sure that someone who is infectious is more likely to be quarantined.
This is not the type of model one can use to capture the actual course of the disease. For that purpose, only a fully fledged model of the type developed by epidemiologists will suffice. Instead, it is a toy model that allows a visualization that helps explain how the more complicated models work. That said, it also has enough structure to offer some insight into two relevant questions that we should be asking:
How much difference does it make to the outcome if the test used to decide who gets isolated has a higher false negative rate. Answer? Very little.
If we contrast a nonspecific policy of social distance with a targeted policy guided by frequent testing that is equally effective at containing the virus, how much more disruptive is the nonspecific policy? Answer? Way more disruptive.
Nicolas Lemann starts his book, Transaction Man, with a reminder:
There are moments in history when everything seems calm, when there isn’t obvious, bitter contention about big questions. It takes some effort now to remember that the dawn of the new millennium was like that, at least to the minds of fortunate people in the United States.
There were disagreements about the relative importance of the different factors that contributed to a century of remarkable progress in the United States, but everyone agreed that progress would continue.
No one then would have predicted that by 2020, life expectancy in the US would be falling.
When I agreed to write a review for Foreign Affairs of Lemann’s book and a complementary book by Binyamin Appelbaum – The Economists’ Hour – I knew that both would ask “what went wrong?” I expected both authors to criticize economists for failing to anticipate the possibility that the US might stop making progress. I anticipated that both would charge that even after the fact, economists have failed to diagnose the cause of the slide from progress to regress.
What I did not anticipate was the claim that economists were the cause.