A novel computational model named SIRSVIDE (Susceptible-Infected-Recovered-Susceptible-Variation-Immune Decay-Immune Escape) has been developed by Prof. Jian Lu's group at Peking University to study the mutation and evolution of viruses. The model incorporates basic principles of epidemiology and key features of viral mutation and evolution, allowing for the prediction of short-term and long-term evolutionary dynamics of viruses.
Understanding the mutation and evolution of viruses (such as SARS-CoV-2) is crucial for effective public health management and response. Traditional epidemiological models often assume that viral transmissibility 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 facing rapidly mutating viruses. To overcome these limitations, Prof. Jian Lu's group at Peking University developed a novel computational model named SIRSVIDE (Susceptible-Infected-Recovered-Susceptible-Variation-Immune Decay-Immune Escape). The SIRSVIDE model not only incorporates basic principles of epidemiology but also integrates key features of viral 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 elements such as viral variation (V), immune decay (ID), and immune escape (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 among different strains, providing a universal framework for studying viral epidemiology and evolutionary dynamics
Mutation Evolution Viruses SARS-Cov-2 Computational Model SIRSVIDE Epidemiology Viral Mutation Viral Evolution Epidemic Trends
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