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Pandemic data quality modelling: a Bayesian approach in the Italian case

Author

Listed:
  • Luisa Ferrari

    (University of Bologna)

  • Giancarlo Manzi

    (University of Milan)

  • Alessandra Micheletti

    (University of Milan)

  • Federica Nicolussi

    (Politecnico di Milano)

  • Silvia Salini

    (University of Milan)

Abstract

When pandemics like COVID-19 spread around the world, the rapidly evolving situation compels officials and executives to take prompt decisions and adapt policies depending on the current state of the disease. In this context, it is crucial for policymakers to always have a firm grasp on what is the current state of the pandemic, and envision how the number of infections and possible deaths is going to evolve shortly. However, as in many other situations involving compulsory registration of sensitive data from multiple collectors, cases might be reported with errors, often with delays deferring an up-to-date view of the state of things. Errors in collecting new cases affect the overall mortality, resulting in excess deaths reported by official statistics only months later. In this paper, we provide tools for evaluating the quality of pandemic mortality data. We accomplish this through a Bayesian approach accounting for the excess mortality pandemics might bring with respect to the normal level of mortality in the population.

Suggested Citation

  • Luisa Ferrari & Giancarlo Manzi & Alessandra Micheletti & Federica Nicolussi & Silvia Salini, 2025. "Pandemic data quality modelling: a Bayesian approach in the Italian case," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(1), pages 87-109, February.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:1:d:10.1007_s11135-024-01913-x
    DOI: 10.1007/s11135-024-01913-x
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    References listed on IDEAS

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    1. Bruno Alessandro Rivieccio & Alessandra Micheletti & Manuel Maffeo & Matteo Zignani & Alessandro Comunian & Federica Nicolussi & Silvia Salini & Giancarlo Manzi & Francesco Auxilia & Mauro Giudici & G, 2021. "CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-20, February.
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    4. Jennifer Beam Dowd & Liliana Andriano & David M. Brazel & Valentina Rotondi & Per Block & Xuejie Ding & Yan Liu & Melinda C. Mills, 2020. "Demographic science aids in understanding the spread and fatality rates of COVID-19," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(18), pages 9696-9698, May.
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