The Costs of Containing H1N1

The Center on Social and Economic Dynamics at Brookings has released a comprehensive report on the economic impact of closing schools and day care centers to help mitigate the infection rate of the H1N1 virus. Center director Joshua Epstein highlights some of the study’s findings and notes that the cost for such closures could be substantial.

TRANSCRIPT

Policymakers always have to compare benefits with costs. The benefits of school closures included reduced mixing, reduced transmission, reduced pressure on the health system, reduced number of cases and so forth. I think the benefits are reasonably clear. What was not clear before this study was the cost side. People had simply not done a calculation of the GDP impact of school closures of various durations and on various scales. The role of the study is to fill that gap and permit policymakers to arrive at a disciplined, informed decision about whether benefits, in fact, outweigh costs. ...

At the moment we are trying to calibrate these models to existing data on prior pandemics, and using the best available estimates of what this disease would look like. That is, to say, in consultation with the Center for Disease Controls, and the National Institute of Health and other agencies. Of course it is not clear how quickly flu could mutate into a really serious disease so we expect something like a thirty percent attack rate (which is a high level of disease). We are talking about tens of millions of cases worldwide. Again, the issue is whether it will mutate into a form more severe than we have seen and whether we can develop a well-matched vaccine to that disease, whether people will adhere to distancing measures to stay home from school, to stay home from work, and so forth. That number (a thirty percent attack rate) assumes business as usual, no interventions, no social distancing, no travel restrictions – none of the things that we would impose. So we hope to do way better than that, but it needs to be understood that if we do not do those things it could be very severe. ...

Classical epidemic model uses differential equations and assumes very well-mixed populations with no particular diversity in the susceptible or infected or recovered groups. Our version of modeling is called agent-based computational modeling, and we basically build artificial societies of software individuals who have different levels of susceptibility, can go through different stages in the disease, can adapt their behavior can exist in social networks that we try to capture. Much more of the social richness and behavioral realism is captured in our form of modeling than in classical epidemiology (although classical epidemiology has given us wonderful and deep insights about disease progression, about the non-linear tipping behavior of epidemics, and has also given wonderful insight into what vaccination strategies should be focused on achieving). ...

You would never base policy on a particular run of the model or a particular evolution. You run these things many, many times and build up a robust statistical portrait of how the disease might progress and address your policy to that. But we know a lot about how people move around, better in the United States than in less-developed countries. In the U.S. we have good data on movement from zip code to zip code, the distribution of trips by distance, we know a lot about international air travel. We have that completely programmed into our models so we have a good idea how people would move around in a day-to-day, business-as-usual scenario. What is a lot less clear is how people will adapt their behavior under the stress and fear of a large scale epidemic. We are seeing (in India for example), at the moment, rapid spread of fear itself. And we have seen this before. There is a famous incident in 1994 in Surat, India where 300,000 people evacuated Surat, a city in India, out of fear of pneumonic plague. In the end not a single case was confirmed by the World Health Organization. So this is also an issue.