Analysis and prediction of COVID-19 based on the SIR-B model

Abstract

With the spread of the global COVID-19 epidemic, an increasing number of asymptomatic infectors are being detected, and are having an increasingly significant impact on the epidemic spread. To address this problem, a modified SIR-B model based on the time-varying is proposed, which takes into account the presence of asymptomatic infectors on the basis of the traditional SIR model, and predicts the impact of asymptomatic infectors on the subsequent development of epidemic by a Particle Swarm Optimization (PSO) algorithm which changes the adaptation function. Simulation experiments show that the SIR-B model has about one-third more infectors than the SIR model, which is closer to the actual situation, and that the SIR-B model is more adaptive and more accurate in predicting the epidemic than the traditional SIR model.