The logistic growth regression model with the genetic algorithm for predicting the third wave of the COVID-19 epidemic in Thailand

Abstract

The world is currently facing the novel coronavirus 2019 (COVID-19). Thailand, with a high basic reproduction number (2.27), the situation remains serious as the disease spreads throughout the country. Applying various control measures to contain the outbreak has increased the need for policymakers to assess the scale of the epidemic. In this study, a logistic growth regression (LGR) model is implemented to characterize the trends and estimate the final size of the third wave of the epidemic in Thailand at both the provincial and national levels. The parameters of the LGR are fine-tuned through the genetic algorithm assisted by the Gauss-Newton algorithm (GA/GNA). The outbreak data from the previous two waves of infection is used to validate the model performance. As a result, the LGR-GA/GNA model provides goodness-of-fit with a low RMSE, high R², and highly significant parameters. Furthermore, when compared to the LGR model parameterized by particle swarm optimization and ant colony optimization, the proposed model outperforms the rest. In addition, to verify the prediction performance by comparing with the Susceptible-Infectious-Recovered (SIR) model, the proposed model improves the prediction accuracy better than the other. As the work was completed on May 6, 2021, the study found a possible increasing trend of COVID-19 for some vulnerable provinces and the whole country and an estimated final and peak size of the epidemic and their occurrences. The study concluded that the epidemic size of the third wave of COVID-19 in Thailand was about 190,000 by mid-July 2021.