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Predicting COVID-19 in very large countries: The case of Brazil

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  • V C Parro
  • M L M Lafetá
  • F Pait
  • F B Ipólito
  • T N Toporcov

Abstract

This work presents a practical proposal for estimating health system utilization for COVID-19 cases. The novel methodology developed is based on the dynamic model known as Susceptible, Infected, Removed and Dead (SIRD). The model was modified to focus on the healthcare system dynamics, rather than modeling all cases of the disease. It was tuned using data available for each Brazilian state and updated with daily figures. A figure of merit that assesses the quality of the model fit to the data was defined and used to optimize the free parameters. The parameters of an epidemiological model for the whole of Brazil, comprising a linear combination of the models for each state, were estimated considering the data available for the 26 Brazilian states. The model was validated, and strong adherence was demonstrated in most cases.

Suggested Citation

  • V C Parro & M L M Lafetá & F Pait & F B Ipólito & T N Toporcov, 2021. "Predicting COVID-19 in very large countries: The case of Brazil," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-15, July.
    • Handle: RePEc:plo:pone00:0253146
    DOI: 10.1371/journal.pone.0253146
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    References listed on IDEAS

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    1. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.
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