Simulation of SIR Deterministic Epidemic Model in Infectious Disease Prediction using R Programming

Abstract

Mathematical models and the statistical models are at present the fundamental elements in planning control and mitigation measures against any future epidemic of an infectious disease. These models allow us to decide from current information about the state and progress of an outbreak, to predict the future, and, most importantly, to quantify the uncertainty in these predictions. In this research paper, we consider a deterministic Susceptible-Infected-Recovered (SIR) epidemic model to disclose a simulation method, and a mathematical model was implemented in the R software environment that allows simulating the spread of infectious disease. Through the aid of the SIR model, data on a wide range of infectious diseases have been analyzed. SIR stands for Susceptible, Infected, and Recovered and indicates the three possible states of the members of the population affected by a contagious disease. SIR model is one of the most effective models which can predict the spreading rate of the virus. We have validated the SIR model with the current spreading rate. The findings of the SIR model can be used to forecast transmission and avoid the disease outbreak. The graphical interface shown in this paper is performed using the R software version 3.4.4.