Abstract
During a fast-moving epidemic, timely monitoring of case counts and other key indicators of disease spread is critical to an effective public policy response. We describe a nonparametric statistical method – originally applied to the reporting of AIDS cases in the 1980s – to estimate the distribution of reporting delays of confirmed COVID-19 cases in New York City. During June 21 – August 1, 2020, the estimated mean delay in reporting was 5 days, with 15 percent of cases reported after 10 or more days. Relying upon the estimated reporting-delay distribution, we project COVID-19 incidence during the most recent three weeks as if each case had instead been reported on the same day that the underlying diagnostic test had been performed. The statistical method described here overcomes the problem of reporting delays only at the population level. The method does not eliminate reporting delays at the individual level. That will require improvements in diagnostic technology, test availability, and specimen processing.
This study relies exclusively on publicly available, aggregate health data that contain no individual identifiers. The author has no competing interests and no funding sources to declare. This article represents to the sole opinion of its author and does not necessarily represent the opinions of the Massachusetts Institute of Technology, the National Bureau of Economic Research, Eisner Health, or any other organization.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
There are no funding sources to declare.
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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
This study relies exclusively on publicly available, aggregate health data that contain no individual identifiers.
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
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Paper in collection COVID-19 SARS-CoV-2 preprints from medRxiv and bioRxiv
The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, University of Washington, and Vrije Universiteit Amsterdam.
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