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Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic

Author

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  • Chirag Modi

    (Berkeley Center for Cosmological Physics, Department of Physics, University of California)

  • Vanessa Böhm

    (Berkeley Center for Cosmological Physics, Department of Physics, University of California
    Lawrence Berkeley National Laboratory)

  • Simone Ferraro

    (Berkeley Center for Cosmological Physics, Department of Physics, University of California
    Lawrence Berkeley National Laboratory)

  • George Stein

    (Berkeley Center for Cosmological Physics, Department of Physics, University of California
    Lawrence Berkeley National Laboratory)

  • Uroš Seljak

    (Berkeley Center for Cosmological Physics, Department of Physics, University of California
    Lawrence Berkeley National Laboratory
    Berkeley Institute for Data Science, University of California)

Abstract

Estimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate mortality from COVID-19. We find that the number of COVID-19 deaths in Italy in 2020 until September 9 was 59,000–62,000, compared to the official number of 36,000. The proportion of the population that died was 0.29% in the most affected region, Lombardia, and 0.57% in the most affected province, Bergamo. Combining reported test positive rates from Italy with estimates of infection fatality rates from the Diamond Princess cruise ship, we estimate the infection rate as 29% (95% confidence interval 15–52%) in Lombardy, and 72% (95% confidence interval 36–100%) in Bergamo.

Suggested Citation

  • Chirag Modi & Vanessa Böhm & Simone Ferraro & George Stein & Uroš Seljak, 2021. "Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22944-0
    DOI: 10.1038/s41467-021-22944-0
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    Cited by:

    1. Benmir, Ghassane & Jaccard, Ivan & Vermandel, Gauthier, 2023. "Optimal monetary policy in an estimated SIR model," European Economic Review, Elsevier, vol. 156(C).
    2. Pedro Bordalo & Giovanni Burro & Katherine Coffman & Nicola Gennaioli & Andrei Shleifer, 2023. "Imagining the Future: Memory, Simulation and Beliefs," Working Papers 701, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Porto, Edoardo Di & Naticchioni, Paolo & Scrutinio, Vincenzo, 2022. "Lockdown, essential sectors, and Covid-19: Lessons from Italy," Journal of Health Economics, Elsevier, vol. 81(C).
    4. Bressan, Alessandro & Duarte Alonso, Abel & Thi Kim Vu, Oanh, 2023. "Business-community relations under COVID-19: A study of micro and small firms," Journal of Business Research, Elsevier, vol. 155(PA).

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