Manski Charles F, Molinari Francesca
Department of Economics and Institute for Policy Research, Northwestern University 2211 Campus Drive, Evanston, IL 60208-2600, USA.
Department of Economics, Cornell University Uris Hall, Ithaca, NY 14853, USA.
J Econom. 2021 Jan;220(1):181-192. doi: 10.1016/j.jeconom.2020.04.041. Epub 2020 May 6.
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.
由于感染检测存在数据缺失以及检测准确性欠佳，所报告的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）病毒累计人群感染率低于实际感染率。因此，所报告的感染后重症发病率高于实际发病率。由于缺乏可信且具信息量的感染率界限，对2019冠状病毒病（COVID-19）大流行时间路径的理解受到了阻碍。本文解释了界定这些感染率的逻辑问题，并利用来自伊利诺伊州、纽约州和意大利的数据报告了说明性结果。我们将这些数据与关于未检测人群感染率以及当前背景下看似可信的检测准确性的假设相结合。我们发现感染率可能远高于所报告的水平。我们还发现，假设死亡报告准确，伊利诺伊州、纽约州和意大利的感染死亡率远低于所报告的水平。
