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Estimating the prevalence of the COVID-19 infection, with an application to Italy

Author

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  • Franco Peracchi

    (Georgetown University, EIEF, and University of Rome Tor Vergata)

  • Daniele Terlizzese

    (EIEF)

Abstract

Knowing the prevalence of the COVID-19 infection in a population of interest, and how it changes over time and across space, is of fundamental importance for public health. Unfortunately, the fraction of cases who turn out to be positive in a test provides a distorted picture of the prevalence of the infection because the tested cases are not a random sample of the population. Since random testing of the population is costly and complicated to carry out, in this note we show how to use the available information, in conjunction with credible assumptions about unknown quantities, to obtain a range of plausible values for the prevalence of the infection. We then apply our method to the Italian data.

Suggested Citation

  • Franco Peracchi & Daniele Terlizzese, 2020. "Estimating the prevalence of the COVID-19 infection, with an application to Italy," EIEF Working Papers Series 2013, Einaudi Institute for Economics and Finance (EIEF), revised May 2020.
    • Handle: RePEc:eie:wpaper:2013
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    References listed on IDEAS

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    1. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    2. James H. Stock, 2020. "Data Gaps and the Policy Response to the Novel Coronavirus," NBER Working Papers 26902, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Augusto Cerqua & Roberta Di Stefano, 2022. "When did coronavirus arrive in Europe?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 181-195, March.
    2. Roy Cerqueti & Raffaella Coppier & Alessandro Girardi & Marco Ventura, 2022. "The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 46-70.
    3. Bollinger, Christopher R. & van Hasselt, Martijn, 2020. "Estimating the cumulative rate of SARS-CoV-2 infection," Economics Letters, Elsevier, vol. 197(C).
    4. Francesco Flaviano Russo, 2020. "Testing Policies During an Epidemic," CSEF Working Papers 591, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.

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