Big data technology in infectious diseases modeling,simulation,and prediction after the COVID-19 outbreak

After the outbreak of COVID-19,the interaction of infectious disease systems and social systems has challenged traditional infectious disease modeling methods.Starting from the research purpose and data,researchers im-proved the structure and data of the compartment model or used agents and artificial intelligence based models to solve epidemiological problems.In terms of modeling methods,the researchers use compartment subdivi-sion,dynamic parameters,agent-based model methods,and artificial intelligence related methods.In terms of factors studied,the researchers studied 6 categories:human mobility,nonpharmaceutical interventions(NPIs),ages,medical resources,human response,and vaccine.The researchers completed the study of factors through modeling methods to quantitatively analyze the impact of social systems and put forward their suggestions for the future transmission status of infectious diseases and prevention and control strategies.This review started with a research structure of research purpose,factor,data,model,and conclusion.Focusing on the post-COVID-19 infectious disease prediction simulation research,this study summarized various improvement methods and analyzes matching improvements for various specific research purposes.

High-Resolution Data on Human Behavior for Effective COVID-19 Policy-Making—Wuhan City,Hubei Province,China,January 1–February 29,2020

Introduction:High-resolution data is essential for understanding the complexity of the relationship between the spread of coronavirus disease 2019(COVID-19),resident behavior,and interventions,which could be used to inform policy responses for future prevention and control.Methods:We obtained high-resolution human mobility data and epidemiological data at the community level.We propose a metapopulation Susceptible-Exposed-Presymptomatic-Infectious-Removal(SEPIR)compartment model to utilize the available data and explore the internal driving forces of COVID-19 transmission dynamics in the city of Wuhan.Additionally,we will assess the effectiveness of the interventions implemented in the smallest administrative units(subdistricts)during the lockdown.Results:In the Wuhan epidemic of March 2020,intra-subdistrict transmission caused 7.6 times more infections than inter-subdistrict transmission.After the city was closed,this ratio increased to 199 times.The main transmission path was dominated by population activity during peak evening hours.Discussion:Restricting the movement of people within cities is an essential measure for controlling the spread of COVID-19.However,it is difficult to contain intra-street transmission solely through citywide mobility restriction policies.This can only be accomplished by quarantining communities or buildings with confirmed cases,and conducting mass nucleic acid testing and enforcing strict isolation protocols for close contacts.

网络化社会中开源创新的激励机制研究

依据网络社会学的嵌入结构理论和社会心理学的相关激励机制理论,应用调查问卷以及网络化社会分析法,采用线上线下相结合方法收集688份有效问卷。创新性地引入嵌入结构,以网络化的各项参与度为自变量,激励机制为因变量,对网络化社会中的开源创新激励机制进行实证研究。研究发现:在网络化与激励机制关系方面,结果性激励和参与性激励与开源创新结果正相关,三种嵌入结构与激励机制也正相关,开源社区发展对开源创新具有显著促进作用。