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

作　　者：Kaichun Jin Xiaolu Tang Zhaohui Qian Zhiqiang Wu Zifeng Yang Tao Qian Chitin Hon Jian Lu 

机构地区：[1]State Key Laboratory of Protein and Plant Gene Research,Center for Bioinformatics,School of Life Sciences,Peking University,Beijing,China [2]NHC Key Laboratory of Systems Biology of Pathogens,Institute of Pathogen Biology,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China [3]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 [4]Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications,Department of Engineering Science,Faculty of Innovation Engineering,Macao University of Science and Technology,Macao,China [5]Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases,KingMed School of Laboratory Medicine,Guangzhou Medical University,Guangdong,China [6]Macao Center for Mathematical Sciences,Macao University of Science and Technology,Macao,China [7]Department of Engineering Science,Faculty of Innovation Engineering,Macao University of Science and Technology,Macao,China

出　　处：《hLife》2024年第5期227-245,共19页健康科学（英文）

基　　金：We thank the National Center for Protein Sciences at Peking University for technical assistance.;This work was supported by grants from the National Key Research and Development Projects of the Ministry of Science and Technology of the People’s Republic of China(2023YFC3041500,2021YFC2301300,and 2021YFC0863400);the National Natural Science Foundation of China(82341110);Beijing Natural Science Foundation(L222009);China Postdoctoral Science Foundation(2023T160010);SLS-Qidong Innovation Fund,and Science and Technology Development Fund of Macao SAR(005/2022/ALC,FDCT0128/2022/A,0020/2023/RIB1,0111/2023/AFJ).

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 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.

关 键 词：viral evolution severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) transmissibility-pathogenicity trade-off immune escape 

分 类 号：R563.1[医药卫生—呼吸系统]