Predicting the evolutionary and epidemiological dynamics of SARS-CoV-2 in South Africa

Since the outbreak of coronavirus disease 2019 (COVID-19), the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continuously mutated and evolved, causing several waves of infection. Predicting the evolutionary and epidemiological dynamics of SARS-CoV-2 remains a challenge. This study combines the epidemic data of different variants of SARS-CoV-2 in South Africa to predict their evolutionary and epidemiological dynamics. Based on the susceptible-infectious-recovered-susceptible (SIRS) transmission dynamics, we consider the transmission rate as an evolutionary trait and the disease-deduced mortality and recovery rates as trade-off functions of the trait. Using the adaptive dynamics method, combined with the epidemic data of the five most recent variants in South Africa, we find that South Africa will be continuously invaded and infected by the new mutant strain with a higher transmission rate. In addition, we find that changing the recovery rate by enhancing treatment, for example, will alter the trade-off function and thereby affect the evolutionary dynamics of SARS-CoV-2, which may evolve into a continuously stable strategy. This study is the first to use evolutionary dynamics theory to predict the future evolutionary and epidemiological dynamics of SARS-CoV-2, which is helpful for the government to predict the epidemic dynamics of COVID-19 and to take effective measures in advance, and it is proposed that advancing treatment time and improving treatment efficiency will contribute to disease control.