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Forecasting COVID-19 Chile’ second outbreak by a generalized SIR model with constant time delays and a fitted positivity rate

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  • Cumsille, Patricio
  • Rojas-Díaz, Óscar
  • de Espanés, Pablo Moisset
  • Verdugo-Hernández, Paula

Abstract

The COVID-19 disease has forced countries to make a considerable collaborative effort between scientists and governments to provide indicators to suitable follow-up the pandemic’s consequences. Mathematical modeling plays a crucial role in quantifying indicators describing diverse aspects of the pandemic. Consequently, this work aims to develop a clear, efficient, and reproducible methodology for parameter optimization, whose implementation is illustrated using data from three representative regions from Chile and a suitable generalized SIR model together with a fitted positivity rate. Our results reproduce the general trend of the infected’s curve, distinguishing the reported and real cases. Finally, our methodology is robust, and it allows us to forecast a second outbreak of COVID-19 and the infection fatality rate of COVID-19 qualitatively according to the reported dead cases.

Suggested Citation

  • Cumsille, Patricio & Rojas-Díaz, Óscar & de Espanés, Pablo Moisset & Verdugo-Hernández, Paula, 2022. "Forecasting COVID-19 Chile’ second outbreak by a generalized SIR model with constant time delays and a fitted positivity rate," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 1-18.
    • Handle: RePEc:eee:matcom:v:193:y:2022:i:c:p:1-18
    DOI: 10.1016/j.matcom.2021.09.016
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    References listed on IDEAS

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    1. Patricio Cumsille & Matías Godoy & Ziomara P Gerdtzen & Carlos Conca, 2019. "Parameter estimation and mathematical modeling for the quantitative description of therapy failure due to drug resistance in gastrointestinal stromal tumor metastasis to the liver," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-27, May.
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    Cited by:

    1. Pájaro, Manuel & Fajar, Noelia M. & Alonso, Antonio A. & Otero-Muras, Irene, 2022. "Stochastic SIR model predicts the evolution of COVID-19 epidemics from public health and wastewater data in small and medium-sized municipalities: A one year study," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).

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