PARAMETER ESTIMATION OF THE EPIDEMIC MODEL FOR FORECASTING THE COVID-19 TRANSMISSION IN THAILAND USING A PARTICLE SWARM OPTIMIZATION ALGORITHM

Abstract

This paper employs the SEAIQRD model framework along with Particle Swarm Optimization (PSO) to demonstrate how control strategies impact predictive COVID-19 models in Thailand. PSO is purposed for accurately determining the model parameters crucial for predicting pandemic transmission. A novel objective function for particle evaluation is constructed using COVID-19 transmission data from Thailand. Intervention strategies consider the sensitivity of these model parameters to prevent spread. The predicted parameters of the model adapt over time to effectively respond to pandemic variations, potentially aiding transmission management. The study estimates parameters of the enhanced SEAIQRD model, incorporating time-varying parameters to adapt to the evolving pandemic. Estimated infection and mortality cases from the model are compared with actual data observed during the 4th and 5th COVID-19 waves in Thailand. These predicted parameters help in forecasting future events and understanding the dynamics of COVID-19 disease.