Monitoring COVID-19 contagion growth

A. Agosto, Alexandra Campmas, Paolo Giudici and A. Renda
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Alexandra Campmas: Larefi - Laboratoire d'analyse et de recherche en économie et finance internationales - UB - Université de Bordeaux

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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.

Keywords: Humans; Models; Covid-19; Human; Sars-Cov-2; Health Care Policy; Health Policy; Statistical; Statistical Model (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

Published in Statistics in Medicine, 2021, 40 (18), pp.4150-4160. ⟨10.1002/sim.9020⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03407115

DOI: 10.1002/sim.9020

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