Comparative Efficiency Analysis of the SIR and SEIR Models Using Numerical Method During Covid-19 Pandemic

Abstract

In January 2020, COVID-19 began spreading in Malaysia from Wuhan, China, and subsequently escalated into a pandemic, having a significant impact on public health development. Hence, scientists use mathematical epidemiological models, such as SIR and SEIR models, to forecast the growth of the spread dynamics of COVID-19 disease to take preventive measures to prevent the outbreak pandemic. This study aimed to analyze and compare both epidemiological models with different numerical methods using actual data. Numerical methods, such as the Euler method, the implicit multistep method, the Fourth-Order Runge-Kutta method, and the Adams-Bashforth-Moulton method, are selected for application in epidemiological models to enhance more accurate predictions of disease spread. The performance results for both epidemiological models, using different numerical methods, are compared with actual data to determine the suitable model and methods that yield the most reliable predictions with the lowest relative error. This comparison is conducted using MATLAB software to assess the efficiency and accuracy of predicting the spread dynamics of COVID-19 during the pandemic in Malaysia in 2021. Hence, the research showed that the prediction of the SIR model in the infected population and the SEIR model with the Fourth Order Runge-Kutta method in the recovered population will produce a higher accuracy analysis result from simulating the prediction of the spread of COVID-19 at every time interval.