Oscillations in U.S. COVID-19 Incidence and Mortality Data Reflect Diagnostic and Reporting Factors

Author:

Bergman Aviv1ORCID,Sella Yehonatan1ORCID,Agre Peter2,Casadevall Arturo2ORCID

Affiliation:

1. Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA

2. Department of Molecular Microbiology and Immunology, Johns Hopkins School of Public Health, Baltimore, Maryland, USA

Abstract

The incidence and mortality data for the COVID-19 data in the United States show periodic oscillations, giving the curve a distinctive serrated pattern. In this study, we show that these periodic highs and lows in incidence and mortality data are due to daily differences in testing for the virus and death reporting, respectively. These findings are important because they provide an explanation based on public health practices and shortcomings rather than biological explanations, such as infection dynamics. In other words, when oscillations occur in epidemiological data, a search for causes should begin with how the public health system produces and reports the information before considering other causes, such as infection cycles and higher incidences of events on certain days. Our results suggest that when oscillations occur in epidemiological data, this may be a signal that there are shortcomings in the public health system generating that information.

Publisher

American Society for Microbiology

Subject

Computer Science Applications,Genetics,Molecular Biology,Modelling and Simulation,Ecology, Evolution, Behavior and Systematics,Biochemistry,Physiology,Microbiology

Reference8 articles.

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4. Kulp AWNBJ. 2016. Characterization of time series data, p 131. In Skiadas CHSC (ed), Applications of chaos theory. CRC Press, Boca Raton, FL.

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