COVID-19 transmission model with discrete time approach

Abstract

In this research, we developed model of SIR for COVID-19 spread. The model is represented by a deterministic discrete-time model. The model is constructed with divided the population into three compartments, namely Susceptible, Infected, and Recovered denoted by S, I, and R, respectively. This research aims to formulate a model for describing the spread of COVID-19 with a data-driven approach. In this research, the model parameters were estimated using the nonlinear least squares method. The data used are daily cases of COVID-19 data in West Java, Indonesia. In addition, other parameters such as birthrate and mortality rate were calibrated using population data and mortality data in the pre-pandemic period. Finally, through numerical simulation, the population dynamics is observed in the model that has been formed based on the estimated parameters.