Tuesday, March 24, 2020

 Coronavirus Data Gaps and the Policy Response to the Novel Coronavirus
  
This note (updated 3/25/20) lays out the basic SIR epidemiological model of contagion, with a target audience of economists who want a framework for understanding the effects of social distancing and containment policies on the evolution of contagion and interactions with the economy. The model is calibrated to the most recent data, however it simple nature abstracts from many important considerations and its output is not intended to supersede estimates from more sophisticated epidemiological models.

This note makes four main points:
  • The effect of social distancing and business shutdowns on epidemic dynamics enters the model through a single parameter, the case transmission rate β. For a specified case transmission rate, the policy design question is how to achieve that case transmission rate while minimizing economic cost. A second economic question is, what is the optimal case path for β, trading off the economic cost of that path against the costs in deaths.
  • The parameters of the model are not well estimated in the literature on the coronavirus because of the lack of available data. Data on prevalence, for example, is obtained from positive rates of testing for the coronavirus, however so far tests have been rationed and almost entirely have been administered to a selected population, those at risk and showing symptoms. Thus, the fraction of tests that are positive do not estimate the population rate of infection.
  • Using Bayes Law, it is possible to re-express the model in terms of β and the asymptomatic rate, which is the fraction of the infected who show sufficiently mild, or no, symptoms so that they are not tested under current testing guidelines. The virtue of re-expressing the model this way is that it makes use of the positive testing rate, on which there is good data. The COVID-19 asymptomatic rate is unidentified in our model and recent estimates in the epidemiological literature range from 0.18 to 0.86. However, the asymptomatic rate could be estimated accurately and quickly by testing a random sample of the overall population.
  • The policy response and its economic consequences hinge critically on the asymptomatic rate. As we illustrate using two policy paths for β, without better knowledge of this knowable parameter, policymakers could make needlessly conservative decisions which would have vast economic costs.
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4 comments:

  1. Good takeaways, thank you. (Please double check your blue-red legend for FIG. 1).

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  2. Very interesting. It's intuitive that the key missing bit of information here is how many infected people are asymptomatic.

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  3. I'd only add the observation the strong non-linearity of health outcomes around some threshold value of I. That is, we need to model what happens when the infected population exceeds the capacity of the health care system.

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    1. That's a great point. That's one of the key issues with a pandemic, and why we're all saying #flattenthecurve. And it explains the mess in Italy.

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