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Superspreading of SARS-CoV-2 in the USA

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  • Calvin Pozderac
  • Brian Skinner

Abstract

A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals is responsible for the majority of new infections. The existence of superspreading implies a fat-tailed distribution of infectiousness (new secondary infections caused per day) among different individuals. Here, we present a simple method to estimate the variation in infectiousness by examining the variation in early-time growth rates of new cases among different subpopulations. We use this method to estimate the mean and variance in the infectiousness, β, for SARS-CoV-2 transmission during the early stages of the pandemic within the United States. We find that σβ/μβ ≳ 3.2, where μβ is the mean infectiousness and σβ its standard deviation, which implies pervasive superspreading. This result allows us to estimate that in the early stages of the pandemic in the USA, over 81% of new cases were a result of the top 10% of most infectious individuals.

Suggested Citation

  • Calvin Pozderac & Brian Skinner, 2021. "Superspreading of SARS-CoV-2 in the USA," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-10, March.
    • Handle: RePEc:plo:pone00:0248808
    DOI: 10.1371/journal.pone.0248808
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    References listed on IDEAS

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    1. James O Lloyd-Smith, 2007. "Maximum Likelihood Estimation of the Negative Binomial Dispersion Parameter for Highly Overdispersed Data, with Applications to Infectious Diseases," PLOS ONE, Public Library of Science, vol. 2(2), pages 1-8, February.
    2. Roman Wölfel & Victor M. Corman & Wolfgang Guggemos & Michael Seilmaier & Sabine Zange & Marcel A. Müller & Daniela Niemeyer & Terry C. Jones & Patrick Vollmar & Camilla Rothe & Michael Hoelscher & To, 2020. "Virological assessment of hospitalized patients with COVID-2019," Nature, Nature, vol. 581(7809), pages 465-469, May.
    3. J. O. Lloyd-Smith & S. J. Schreiber & P. E. Kopp & W. M. Getz, 2005. "Superspreading and the effect of individual variation on disease emergence," Nature, Nature, vol. 438(7066), pages 355-359, November.
    4. Alison P. Galvani & Robert M. May, 2005. "Dimensions of superspreading," Nature, Nature, vol. 438(7066), pages 293-295, November.
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