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

摘要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 im-mune escape.In this context,we have developed a novel computational model named SIRSVIDE(Susceptible-In-fected-Recovered-Susceptible-Variation-Immune Decay-Immune Escape)to simulate the transmission and evolu-tionary 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 fluctua-tions.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 comprehen-sive framework for generating both short-and long-term viral epidemiological and evolutionary dynamics.The simula-tion outcomes align with existing evidence indicating that SARS-CoV-2 is undergoing selection for heightened trans-missibility,decreased pathogenicity,and enhanced immune escape.Furthermore,the model sheds light on the possible evolutionary dynamics of other viruses.

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作者 Kaichun Jin [1] Xiaolu Tang [1] Zhaohui Qian [2] Zhiqiang Wu [2] Zifeng Yang [3] Tao Qian [4] Chitin Hon [5] Jian Lu [1] 学术成果认领
作者单位 State Key Laboratory of Protein and Plant Gene Research,Center for Bioinformatics,School of Life Sciences,Peking University,Beijing,China [1] NHC Key Laboratory of Systems Biology of Pathogens,Institute of Pathogen Biology,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China [2] State Key Laboratory of Respiratory Disease,National Clinical Research Center for Respiratory Disease,Guangzhou Institute of Respiratory Health,The First Affiliated Hospital of Guangzhou Medical University,Guangdong,China;Respiratory Disease Al Laboratory on Epidemic and Medical Big Data Instrument Applications,Department of Engineering Science,Faculty of Innovation Engineering,Macau University of Science and Technology,Macau,China;Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases,KingMed School of Laboratory Medicine,Guangzhou Medical University,Guangdong,China [3] Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases,KingMed School of Laboratory Medicine,Guangzhou Medical University,Guangdong,China;Macau Center for Mathematical Sciences,Macau University of Science and Technology,Macau,China [4] Respiratory Disease Al Laboratory on Epidemic and Medical Big Data Instrument Applications,Department of Engineering Science,Faculty of Innovation Engineering,Macau University of Science and Technology,Macau,China;Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases,KingMed School of Laboratory Medicine,Guangzhou Medical University,Guangdong,China;Department of Engineering Science,Faculty of Innovation Engineering,Macau University of Science and Technology,Macau,China [5]
栏目名称 ARTICLE
DOI 10.1016/j.hlife.2024.03.006
发布时间 2024-09-09
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