Forecasting COVID-19 Chile’ 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.

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

“…Epidemiological compartmental deterministic models, like the Susceptible-Infected-Recovered (SIR) model firstly described by [5] (and extended versions of it) have been employed to predict COVID-19 spread [6] , [7] , [8] , [9] , [10] . However, predictability issues arise and models (whether they are phenomenological, mechanistic, or agent-based) are not efficient predict the COVID-19 pandemics in the long term [11] , [12] .…”

Estudio de la pandemia COVID-19 en México usando un modelo compartimental con retardos

“…Aquí, una enfermedad infecciosa se podrá clasificar como epidemia si R 0 > 1, se extinguirá cuando R 0 < 1 y permanecerá constante si R 0 = 1, ver (Cumsille et al, 2022). Con base al modelo (2) se diseña un observador de estado del tipo Luenberger, con la finalidad de poder estimar/reconstruir la dinámica de las poblaciones de recuperados y susceptibles, dado que no se cuenta con información reportada por las organizaciones de salud.…”

“…Una de las herramientas más utilizadas para describir la dinámica de la propagación de una enfermedad infecciosa en la población, son los conocidos modelos matemáticos epidemiológicos. Estos modelos permiten conocer el impacto de las enfermedades infecciosas, dependiendo de su capacidad de transmisión, de sus medios de trasmisión y del tamaño de la población susceptible e infecciosa (Angulo et al, 2020;Cumsille et al, 2022;Goel y Sharma, 2020;Volpert et al, 2020).…”

An innovative ensemble model based on multiple neural networks and a novel heuristic optimization algorithm for COVID-19 forecasting

“…Traditional epidemiological models have been widely adopted in predicting COVID-19 cases. The time-dependent susceptible, infectious, and/or recovered (SIR) model is frequently used to model the growth of COVID-19 and to predict the future condition of infection and recovery rates ( Alenezi et al, 2021 , Cumsille et al, 2022 , Masuhara and Hosoya, 2021 ). In addition, many studies also used the susceptible, exposed, infectious, and/or recovered (SEIR) model for COVID-19 epidemic prediction ( Annas et al, 2020 , Das et al, 2021 , Paul et al, 2021 , Piovella, 2020 ).…”