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Monitoring COVID-19 contagion growth

Author

Listed:
  • A. Agosto
  • Alexandra Campmas

    (Larefi - Laboratoire d'analyse et de recherche en économie et finance internationales - UB - Université de Bordeaux)

  • P. Giudici
  • A. Renda

Abstract

We present a statistical model that can be employed to monitor the time evolution of the COVID-19 contagion curve and the associated reproduction rate. The model is a Poisson autoregression of the daily new observed cases and dynamically adapt its estimates to explain the evolution of contagion in terms of a short-term and long-term dependence of case counts, allowing for a comparative evaluation of health policy measures. We have applied the model to 2020 data from the countries most hit by the virus. Our empirical findings show that the proposed model describes the evolution of contagion dynamics and determines whether contagion growth can be affected by health policies. Based on our findings, we can draw two health policy conclusions that can be useful for all countries in the world. First, policy measures aimed at reducing contagion are very useful when contagion is at its peak to reduce the reproduction rate. Second, the contagion curve should be accurately monitored over time to apply policy measures that are cost-effective. © 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Suggested Citation

  • A. Agosto & Alexandra Campmas & P. Giudici & A. Renda, 2021. "Monitoring COVID-19 contagion growth," Post-Print hal-03407115, HAL.
    • Handle: RePEc:hal:journl:hal-03407115
    DOI: 10.1002/sim.9020
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    Cited by:

    1. Ray, Evan L. & Brooks, Logan C. & Bien, Jacob & Biggerstaff, Matthew & Bosse, Nikos I. & Bracher, Johannes & Cramer, Estee Y. & Funk, Sebastian & Gerding, Aaron & Johansson, Michael A. & Rumack, Aaron, 2023. "Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1366-1383.
    2. Marco Bonetti & Ugofilippo Basellini, 2021. "Epilocal: A real-time tool for local epidemic monitoring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(12), pages 307-332.
    3. Ugofilippo Basellini & Carlo Giovanni Camarda, 2020. "Modelling COVID-19 mortality at the regional level in Italy," Working Papers axq0sudakgkzhr-blecv, French Institute for Demographic Studies.

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