Mathematical Modeling and Epidemic Prediction of COVID-19 of the State of São Paulo, Brazil

Abstract

The Coronavirus pandemic, COVID-19, has taken the lives of several thousand people worldwide, causing many regions and countries to order the closure of their borders, with yet unpredictable global consequences. This pandemic represents a global public health emergency that has never been seen in recent times. In this work, we conducted an investigation of the current coronavirus outbreak (COVID-19) in the State of São Paulo, Brazil, from a mathematical modeling perspective. We seek to describe the different paths of dynamic transmission of infection, the size of the epidemic in the State of São Paulo and to emphasize the role of strict isolation policy in the transmission and dissemination of COVID-19. This research is based on a new mathematical model that is an extension of the SEIR family of compartmental models. As a result of the computational simulations, we show how the social measures of isolation, quarantine and treatment/monitoring of the infected individuals can influence the model parameters and change the cases rates of infected individuals and individuals exposed over time. The predictive capacity of this model, like most mathematical models, is limited by the accuracy of the available data. Problems regarding the accuracy, standard and reliability of the available data are beyond the scope of this work.