Optimization-Based Method for Estimating the Transmission Rate of Covid-19 during the Lockdown in Malaysia

ABSTRACT

In this work, the optimization-based method is implemented to investigate the effectiveness of lockdown strategies undertaken to contain the COVID-19 during the first two waves in Malaysia. The well-known Susceptible-Infected-Removed (SIR) epidemiological model was fitted to the actual data of infected cases from the official press to closely reflect the observed COVID-19 outbreak in Malaysia. The Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were implemented to determine the daily transmission rate beta(t) that fits the SIR model to the actual data. The best fitness value of PSO is mostly stable at approximately 37.5 with the best value of 37.41 at a population size of 1000, whilst the best value for GA slowly decreased to the best value of 47.45 at a population size of 1000. In addition, PSO requires a lower number of iterations to reach the optimum fitness value for the same population size as compared to GA, while GA is too far to reach the convergence. As the removal rate (gamma) is a constant value fixed at 0.1, the optimized beta(t) values indicate a high basic reproduction number (average R0 = 1.23) obtained before the Movement Control Order (MCO), followed by a considerable decrease to an average R0 value of 1.23 during the MCO. During the Conditional MCO and Recovery MCO, the basic reproduction number was slightly decreased to an average R0 value less than 1. This is an indication of the success of the government to contain the pandemic during the first two waves as the R0 has been kept below than 1.