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

TL;DR

This paper develops a generalized SIR model with constant time delays and a fitted positivity rate to forecast COVID-19's second outbreak in Chile, providing a clear methodology for parameter optimization and pandemic trend prediction.

Contribution

It introduces a robust, reproducible approach combining a generalized SIR model with positivity rate fitting for accurate COVID-19 outbreak forecasting.

Findings

Successfully reproduces infection curves for Chilean regions.

Qualitatively forecasts the second outbreak and fatality rate.

Distinguishes reported and real COVID-19 cases.

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.