The new model provides an integrated framework for studying viral epidemiology and evolutionary dynamics

Understanding the mutation and evolution of viruses (such as SARS-CoV-2) is critical to effective public health management and response. Traditional epidemiological models often assume that viral infectivity and pathogenicity remain constant during disease transmission, ignoring the fact that viruses continuously evolve through natural selection and random mutations. This simplification limits the accuracy of these models in predicting epidemic trends, especially when dealing with rapidly mutating viruses.

To overcome these limitations, Professor Jian Lu’s team at Peking University developed a new computational model called SIRSVIDE (Susceptible-Infected-Shumbled-Sensitive-Variation-Immune Decay-Immune Evasion). The SIRSVIDE model not only incorporates basic principles of epidemiology but also incorporates key features of virus mutation and evolution. By simulating the dynamics of susceptible (S), infected (I), recovered (R) populations, and the process of individuals becoming susceptible again (S), while introducing factors such as viral variation (V), the decay of immune response (ID), and immune evasion (IE), the model can capture both short-term and long-term evolutionary dynamics of viruses. It considers not only the evolution of individual strains but also the competitive relationships between different strains, providing a global framework for the study of virus epidemiology and evolutionary dynamics.

Simulations under specific conditions (high mutation rate m= 10^-8large host population N = 10⁹) showed that viral populations undergo continuous lineage iterations and evolve towards increased infectivity, enhanced immune evasion and reduced pathogenicity, accompanied by significant short-term fluctuations in viral traits. The study found that large host populations and high mutation rates are key factors driving these unique evolutionary trends.

Despite these long-term evolutionary trends, the inherent randomness of virus evolution inevitably leads to short-term fluctuations in virus traits. Simulations under various parameters showed that a significant percentage (27.12%-37.59%) of the prevalent strains have higher transmissibility and pathogenicity compared to their ancestral strains. This suggests that new variants with both enhanced transmissibility and pathogenicity may emerge in the short term of an epidemic. Furthermore, as the number of infections or the mutation rate decreases, the uncertainty about the short-term evolutionary direction of viruses increases further.

The classic ‘transmissibility-infectiousness trade-off’ hypothesis proposed by Andersen & May in 1982 suggests that there is a trade-off between transmissibility and pathogenicity in virus evolution. However, the transmission routes and pathogenic mechanisms of viruses are different, and transmissibility and pathogenicity are not always strictly linked. The SIRSVIDE model provides a dynamic analytical framework that comprehensively examines factors such as susceptible-infected-recovery-susceptibility dynamics, immune decay, immune escape, and virus mutation, allowing in-depth analysis of the impact of different parameter changes on evolutionary dynamics of the virus. This helps us understand how viruses balance transmissibility and pathogenicity under multiple selection pressures to find the optimal adaptation strategy.

In summary, the SIRSVIDE model developed by Professor Jian Lu’s group provides a comprehensive framework for the study of viral epidemiology and evolutionary dynamics. The simulation results reveal that under certain conditions, viral populations tend to evolve towards increased infectivity, enhanced immune evasion and reduced pathogenicity, with large susceptible host populations and high mutation rates being key factors driving this evolutionary trend. At the same time, the inherent randomness of virus evolution leads to short-term fluctuations in virus characteristics. These findings are consistent with the evolutionary evidence of SARS-CoV-2 and provide new insights for investigating the potential evolutionary patterns of other viruses, which is very important for guiding public health policymaking.