Nonetheless, the downside and potentially unavoidable consequence of moving to a testing economy in this way is that it will reduce social cohesion. Just as the beef producers who worried that having some producers become certified as BSE safe would cause producers who were not certified to be seen as unsafe, we should be concerned that not being certified safe might become stigmatized with all of the costs that entails.
How Safe Is Safe Enough?
The discussion here thus far has glossed over an important issue with any kind of test: that it is imperfect. In particular, a test conducted on a person who has COVID-19 can return negative—this is a false negative—while a test conducted on a person who doesn’t have the virus can be returned positive—a false positive. This impacts on policies that are contingent on test results.
Recall that with a HAVE test, what we want to do is isolate those who test positive and release those who test negative. We are doing this to prevent having to isolate everyone. Thus, if a person has a false positive, relative to the fact that our plan was to isolate that person anyhow, the fact that we choose to isolate them impacts them but not by much relative to the alternative. By contrast, if a person has a false negative, our plan is to release that person from isolation. In that case, however, we are putting someone we wanted to isolate into the population. Suffice it to say, that is costly.
But is it so costly that we should not use a “test, then release” strategy? Typically, there is a trade-off between false positive and false negative rates, with one rising while the other falls. Often this is because a test is a test not just for one factor but for the presence of multiple factors. So, if your test involves looking for the presence of, say, three factors, then you might choose to conclude that the test is positive only if all three factors are present.17 That means that, given this approach, you are less likely to have a false positive test but more likely to have a false negative test. This along with improperly done nose swabs is why for many COVID-19 tests there was a reported false negative rate of between 10 and 15 percent (in line with other viruses) but a false positive rate of only 1 percent.18
One reason many tests appear to err on the side of minimizing false positives is because antiviral treatments might be harmful to some patients or otherwise involve costs, and you do not want to use them on people who do not have a particular virus. By contrast, a false negative test can be followed up with a future test for that patient that may reverse the finding. In other words, you want to be confident that you are treating the right person, and if you have the option to continue observation and test, you may then be comfortable perhaps initially missing a treatable person.
This weight of characteristics changes when you are dealing with a different decision—whether to release a potentially infectious person. In that case, you would want to err on the side of minimizing false negatives. If you want to release someone who has tested negative, you may not have an option to retest them before they do more harm. By contrast, if someone tests positive falsely, you can keep them isolated and then retest them later. This same logic applies to both HAVE and HAD tests but is stronger for HAD tests as the goal is not to retest using that regime. By contrast, a HAVE regime would involve repeated testing of people who returned negative results in the past.
This suggests that our medical practices will need to be informed by the decisions that have to be made—treatment versus release—to an extent that we haven’t done to this date. Of course, it goes without saying that tests that can reduce both false positives and false negatives will be more valuable as well. Interestingly, however, our tolerance for tests with errors may be greater than would be apparent at first. For instance, Nobel laureate economist Paul Romer conducted simulations of the movement of infectious diseases like COVID-19 through the population and compared the use of a blanket isolation strategy versus a test and release strategy even when tests had high degrees of false negatives.19 His analysis suggested that even tests with a false negative rate of 20 percent or more could lead to two or three times fewer people eventually infected than a no-isolation approach but also involves fewer people required to be in isolation when even imperfect tests are used.
It is not hard to see why targeting the isolation based on test results reduces the total number of people in isolation. What matters for controlling the infection is how many infectious people it isolates. If people are isolated at random, you have to isolate a lot more to get the same number of people who are infectious.20
The good news here is that, while we may want to calibrate test efficacy for the decision made, there is substantial room for error to still have a substantive impact. In other words, a more perfect test is better but not that much better than an imperfect one.21 Nonetheless, even with very intense testing (say, everyone being tested once every two weeks), this will likely only reduce the intensity of required social distancing and contact tracing. That said, as those activities are the costliest for the economy, it is likely that the social rate of return to widespread testing will be very high. More practically, it is likely the best option would be to be sensible in how tests are allocated and conducted.22 For instance, in situations where households have been locked down for a month or more, perhaps only one member of a household needs to be tested.
What If It’s Worse?
Everything in this book thus far, as well as policy discussions regarding COVID-19, has been based on a very important assumption: once you have contracted the virus and recovered, you are immune. It is for this reason that epidemiologists focus on a sufficient share of the population obtaining immunity from COVID-19 either by past infection or as a result of a future vaccine. If you do this, then even with normal physical interactions, the virus eventually dies out (as R0 becomes less than 1). In particular, this is why we can talk about HAVE and HAD testing as making people safe again. So, while the crisis is awful, the promise of immunity gives us hope.
What if that hope is unfounded? What if you are not immune even if you have contracted the virus? What if a vaccine is not possible for the same reason? In this case, epidemiologists no longer use the SIR (susceptible–infected–removed) model, as there are no recovered people who are not able to infect others. Instead, we must use the SIS (susceptible–infected–susceptible) model. In that situation, when R0 exceeds one, the virus never goes away and a share of the population is always infected.23 The only way to get rid of the virus is by extreme measures—for instance, socially distancing until there are no more infected people or by coming up with treatments such that we don’t care if people are infected or not.24
Is this outcome possible for COVID-19? Because the virus is relatively new, at the time of writing, it is hard to be sure.25 Because recovering from COVID-19 required antibodies, scientists were optimistic that such antibodies would give immunity for some period of time. However, in April 2020, South Korea reported 111 coronavirus patients testing positive again after they had recovered (and tested negative twice in a twenty-four-hour period).26 One possibility is that the negative tests were false negatives. Another is that the virus has reactivated. This is a virus that is latent for a time and but remains inside the cells of the host. This happens with chickenpox, which can decades later reactivate in adults as shingles. Finally, there could be reinfection. This is why the flu is persistent. The antibodies provide immunity only for a time and not against alternative strains of the virus. Coronaviruses are a relatively recent phenomenon, so a lack of immunity remains a possibility.