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

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

Mathematics and Computers in Simulation (MATCOM), 2022, vol. 193, issue C, 1-18

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.

Keywords: Mathematical modeling; Parameter estimation method; Predictive modeling; Computational model; Epidemiological modeling (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:193:y:2022:i:c:p:1-18

DOI: 10.1016/j.matcom.2021.09.016

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