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

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. Highlights• We developed a clear, efficient, and reproducible methodology for parameter optimization to calibrate real data.• We also provided a suitable generalized SIR model, including a fitted positivity rate.• Our methodology reproduces the general trend of the infected' curve for both the reported and the real cases.• It can also forecast different scenarios of the spreading and the infection fatality rate of COVID-19 qualitatively.