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Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem

Charles F. Manski, Francesca Molinari

arXiv 13 Apr 2020 · Econometrics · publishedJournal of Econometrics (2020) · 172 citations (OpenAlex)

arXiv:2004.06178 · PDF · DOI · OpenAlex

Abstract

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.

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Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

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4Measuring Diagnostic Test Performance Using Imperfect Reference Tests: A Partial Identification Approach0.64422
5Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network0.51121
6Sparse HP Filter: Finding Kinks in the COVID-19 Contact Rate0.40511
7Estimation of Covid-19 Prevalence from Serology Tests: A Partial Identification Approach0.40511
8Bounding Infection Prevalence by Bounding Selectivity and Accuracy of Tests: With Application to Early COVID-190.40511
9Policy Evaluation during a Pandemic0.40511
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