Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem
As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Italy is substantially lower than reported.
We thank Yizhou Kuang for able research assistance. We thank Michael Gmeiner, Valentyn Litvin, and Jörg Stoye for helpful comments. We are grateful for the opportunity to present this work at an April 13, 2020 virtual seminar at the Institute for Policy Research, Northwestern University. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
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Charles F. Manski & Francesca Molinari, 2020. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, . citation courtesy of