Modeling viral evolution: A novel SIRSVIDE framework with application to SARS-CoV-2 dynamics


Prof. Jian Lu published a paper in hLife.

Understanding evolutionary trends in emerging viruses, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is crucial for effective public health management and response. Nonetheless, extensive debates have arisen concerning viral evolutionary trends, particularly the interplay between transmissibility, pathogenicity, and immune escape. In this context, we have developed a novel computational model named SIRSVIDE (Susceptible-Infected-Recovered-Susceptible-Variation-Immune Decay-Immune Escape) to simulate the transmission and evolutionary dynamics of viral populations. Our simulation results indicate that under conditions of high mutation rates, elevated transmission rates, and larger susceptible host populations, viral populations exhibit prolonged increases in transmissibility and immune escape, accompanied by reductions in pathogenicity and noticeable short-term fluctuations. However, when the total susceptible population size and mutation rate decrease, substantial uncertainty in the evolutionary trends of viral populations becomes apparent. In summary, the SIRSVIDE model establishes a comprehensive framework for generating both short- and long-term viral epidemiological and evolutionary dynamics. The simulation outcomes align with existing evidence indicating that SARS-CoV-2 is undergoing selection for heightened transmissibility, decreased pathogenicity, and enhanced immune escape. Furthermore, the model sheds light on the possible evolutionary dynamics of other viruses.

Original link: https://www.sciencedirect.com/science/article/pii/S2949928324000221.