The most infectious disease in modern times was measles, with an R0 between 12 and 18.4 This is because it could spread in the air. The usual influenza we experience each year is between 0.9 and 2.1. Some years are good, while others are bad. The SARS outbreak was between 2 and 5, while Ebola, which is transmitted via bodily fluids, was between 1.5 and 2.5. You can see both significant variation but also significant ranges of uncertainty. For Ebola, this was likely related to population density. At the time of writing, COVID-19 has an estimated R0 between 1.4 and 3.9. It is for this reason that many predicted that, left unchecked, 70 percent of all people would eventually contract the virus.
The Human Equation
The interesting thing about R0 is that it is not just a biological number—that is, related to how a virus can move and bind itself to others—but also a social number.5 If a hermit contracts the measles, then R0 is 0. If a partygoer gets it, R0 is much higher. The estimates of R0 are averages, which is a guide to decision-making but not what you want to know. In principle, you want to know everyone’s specific R0 and you likely want to draw your attention to reducing the R0s of those who are at the top of this list.
Rather than individual R0s, the best we can hope for are group R0s. For instance, children move about, keep personal hygiene, and live their lives in a very different way from other beings. As any parent with young kids knows, there are years in which your house turns into the town from Albert Camus, The Plague, sans any widespread epidemic. This is why, in many countries, the first step in social distancing was to shut down schools. This wasn’t because children are especially at risk—they aren’t, thank goodness—but because they are “vectors”—an identifiable group known to have potentially high R0s. The same is true of college students. If most students stayed at college, they were likely to be strong vectors for infection because they spend their days going from numerous gatherings of a hundred people or more before bringing it all back to others in their dorms. By contrast, office workplaces are potentially lower-risk.
The epidemiological models consider who might interact with whom when they try to predict the spread of an infection, but those assumptions are “hard-wired” into their models. Economists (and other social scientists) typically shy away from predictions based on such hard-wired behavior. Instead, when considering how people might interact with one another, they look to their choices. People do not blindly react to pandemics and continue to go about their daily business. Nor do they hide out for the duration. What they do is balance the risk of interactions as the pandemic progresses, based on information they have at hand. In other words, what epidemiological models can miss is that humans change their behavior over time, and this can impact the mathematics of the infection.
The research that integrates economics into epidemiology is very much nascent. However, from the work that has been done to date, some important insights can be drawn. First of all, we can expect that when people are concerned about the costs of being infected, they won’t necessarily need to be told to socially distance themselves from others.6 In particular, as the infection rate starts to climb, more people will reduce their economic activity, which has the effect of moderating the spread of any virus. During the 2009 H1N1 epidemic, people in the United States reduced their time spent among others,7 and similarly in Mexico, although there the behavior differed among different socioeconomic groups, with poorer groups adjusting less.8
Second, it is possible that the behavioral response to a pandemic can cause the peak infection level to be lower than what might otherwise emerge from a standard epidemiological model.9 This is because, as the infection rate increases, people will perceive greater risk from interacting with others. While that reduces the infection rate, this back and forth will slice the top off the peak but spread the length of the pandemic further; that is, it will “flatten the curve” (discussed in more detail in chapter 3).
This has another important implication that can test our usual epidemic intuition. If a virus is more virulent (that is, can be more easily passed between people), the usual prediction is that a larger share of the population will become infected (as R0 is relatively high). However, once the human element is taken into account, this could go the other way. If it was known that a virus was particularly virulent, people would fear going out and would socially distance. The more virulent it is, the more people will self-isolate to avoid others. This could well mean that virulent outbreaks have a lower total number infected than less virulent ones. This is, of course, just a theoretical possibility at this stage, but there is anecdotal evidence in the COVID-19 outbreak that certain groups—particularly, younger people who have less to fear from the consequences of being infected—do not practice social distancing as much as others.10
While people might reduce their social interactions out of fear, it is important to emphasize that this may still be too little relative to what we might all agree would be in the collective interest. That is because people take into account their own fear in refraining from social interactions but not the impact those actions might have on others. In other words, fear is not necessarily enough, and governments may have to take heavy-handed actions to influence R0.11
The good news is that policy actions designed to change the behavior of many can have an impact. This was starkly demonstrated in a comparative study of the Philadelphia and St. Louis responses to the flu pandemic of 1918.12 As figure 2.1 (drawn from that study) demonstrates, St. Louis had a milder and prolonged epidemic compared with Philadelphia, which had the majority of cases in just one month. The difference between the two was that Philadelphia held a parade of returning soldiers from World War I, while St. Louis, armed with the same health warnings, closed schools and even churches and banned gatherings of more than 20 people. As network economist Matthew Jackson notes, being able to reduce the number of highly connected clusters within a network of social relationships can dramatically reduce R0.13
Figure 2.1
Pandemic of 1918. Source: Richard J. Hatchett, Carter E. Mecher, and Marc Lipsitch, “Public Health Interventions and Epidemic Intensity during the 1918 Influenza Pandemic,” Proceedings of the National Academy of Sciences 104, no. 18 (May 2007): 7582–7587 (doi: 10.1073/ pnas.0610941104).
While we understand the general science behind disease transmission, the mix of biological and social factors for each new disease means that we have broad ranges for R0 and scant details about what any particular measure might do to the spread of the virus. That said, we know that if we shut everything down, then we can minimize any given R0. In doing so, we maximize the R0 within a given household, but the idea is to keep the spread between households at a minimum. How much we want to do this depends both on the degree of the problem—how high R0 would otherwise be—and on the costs of becoming infected versus the costs associated with trying to reduce R0.
Willingness to Act
This leads us to the costs. The potential health costs of COVID-19 are of primary interest. As I apply my economist filter to what I understand of the biomedical properties here, I see those health costs (in terms of likely medical care) in four groups. The first are the people who contract the virus but have no important symptoms. They create no health costs at all. The second are people who contract the virus and have symptoms akin to a severe flu. The health costs here are primarily in terms of lost ability to work and function. The third are those who have severe enough symptoms to require hospitalization with the obvious associated costs. The final category is those for whom COVID-19 proves to be fatal. Early estimates from China suggested that 81 percent of those who tested positive for COVID-19 were in the first two categories. Of the remainder, 14 percent were severe, and 5 percent were critical. The remaining 2.3 percent had died.14