Optimization of Sample Size and Epidemic Model Parameters by Using the Differential Evolution and Optimization Algorithms

Abstract

This research work focuses on epidemic surveillance by using SIR model. The objective of this research is to optimize the parameters of an epidemic model and its sample size. As any surveillance requires a proper sample size, the calculated sample size should be sufficient for the surveillance; otherwise it will not be useful for the disease analysis and decision making process. In this research, differential evolution algorithm and optimization algorithm are employed to optimize the sample size and the parameters of SIR model respectively. Hence, the “optim” functions are used to find optimum value of susceptible, infected and transmission rate at the initial state.