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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 181-192 |
| Number of pages | 12 |
| Journal | Journal of Econometrics |
| Volume | 220 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2021 |
Funding
We thank Yizhou Kuang for able research assistance. We thank Mogens Fosgerau, Michael Gmeiner, Valentyn Litvin, John Pepper, Jörg Stoye, Elie Tamer, and an anonymous reviewer 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. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Keywords
- Epidemiology
- Missing data
- Novel coronavirus
- Partial identification
ASJC Scopus subject areas
- Economics and Econometrics
