Estimation of Parameters in the SIR Epidemic Model Using Particle Swarm Optimization

  • Putra S
  • Mu'tamar K
  • Zulkarnain K
N/ACitations
Citations of this article
26Readers
Mendeley users who have this article in their library.

Abstract

Susceptible, Infected and Resistant (SIR) models are used to observe the spread of infection from infected populations into healthy populations. Stability analysis of the model is done using the Routh-Hurwitz criteria, basic reproduction number or the Lyapunov Stability. For stability analysis, parameters value are needed and these values are usually assumed. Given data cannot be used to determine the parameter values of SIR model because analytic solution of system of nonlinear differential equation cannot be determined. In this article, we determine the parameters of the exponential growth model, logistic model and SIR models using the Particle Swarm Optimization (PSO) algorithm. The SIR model is solved numerically using the Euler method based on the parameter values determined by PSO. The simulation results show that the PSO algorithm is good enough in determining the parameters of the three models compared to analytical methods and the Gauss-Newton's method. Based on the average hypothesis test the relative error obtained from the PSO algorithm to determine the parameters is less than 3% with a significance level of 1%.

Cite

CITATION STYLE

APA

Putra, S., Mu'tamar, K., & Zulkarnain, K. (2019). Estimation of Parameters in the SIR Epidemic Model Using Particle Swarm Optimization. American Journal of Mathematical and Computer Modelling, 4(4), 83. https://doi.org/10.11648/j.ajmcm.20190404.11

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free