Prediction of Covid-19 transmission by SIRS Model using 3-step predictor-corrector method

Abstract

Effective and accurate prediction of the COVID-19 rate is vital for effective public health monitoring and intervention, but forecasting models are often hindered when it comes to striking a balance between accuracy and computing efficiency. This often calls for better prediction models that can effectively capture the dynamics of transmission and can serve as an important tool for healthcare policymaking. This study introduces a hybrid model combining the 3-step Adams-Bashforth-Moulton (ABM) method with the Runge-Kutta (RK4) method to analyze and forecast COVID-19 transmission rates in Malaysia. The hybrid model utilize the RK4 method for generating initial solutions and the ABM method for refining predictions, which is then used to solve the SIRS compartmental using Malaysia-specific COVID-19 data, including confirmed cases, recoveries, deaths, population size, and contact rates. The hybrid RK4-ABM model demonstrates enhanced accuracy in predicting COVID-19 transmission rates. By combining the computational efficiency of RK4 with the accuracy of ABM, the model delivers improved forecasting performance over time. The study will be of massive contribution to epidemiological research by demonstrating the RK4-ABM model's effectiveness in predicting COVID-19 transmission rates and providing valuable insights for healthcare policymakers in Malaysia. This hybrid RK4-ABM model shows potential for future epidemic modeling and forecasting, highlighting the importance of mathematical approaches in understanding and controlling pandemic impacts.