Estimating the COVID-19 infection rate: Anatomy of an inference problem

Charles Manski and Francesca Molinari

Journal of Econometrics, 2021, vol. 220, issue 1, 181-192

Abstract: As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of cumulative 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, assuming accurate reporting of deaths, the infection fatality rates in Illinois, New York, and Italy are substantially lower than reported.

Keywords: Partial identification; Missing data; Epidemiology; Novel coronavirus (search for similar items in EconPapers)
JEL-codes: C14 C82 I19 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407620301676
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem (2020)
Working Paper: Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem (2020)
Working Paper: Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:220:y:2021:i:1:p:181-192

DOI: 10.1016/j.jeconom.2020.04.041

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().