SIR epidemic dynamics in populations with biased mixing: complex network-based approach

报告题目：SIR epidemic dynamics in populations with biased mixing: complex network-based approach

报 告 人：王毅  教授  中国地质大学（武汉）

邀请人：白振国

报告时间：2026年1月10日  9：00-10：00

地点：腾讯会议 637-423-292; 密码：123456

报告人简介：王毅，中国地质大学（武汉）数学与物理学院副院长，教授、博士生导师，主要研究方向为生物数学与复杂网络。主持国家自然科学基金3项、湖北省自然科学基金等项目6项，在BMB、Physica D、Chaos、DCDS-B和JMB等国内外期刊发表论文多篇，合作出版专著3部。

报告摘要： Individuals in a population may have biased mixing, which could be described by networks with nontrivial degree correlations. For example, many social networks show that high degree nodes tend to preferably connect with other high degree nodes, the so called “assortative mixing” property. In this topic, I first review some SIR epidemic dynamic models with degree correlations, and compare simulation results on degree correlated networks with SIR dynamics on configuration type networks; then proposed an edge-based SIR epidemic model in degree correlated networks, with the basic reproduction number and final epidemic size being equivalent to those using percolation theory; furthermore, we discuss the relationship between the basic reproduction number on configuration type networks and that in degree correlated networks. In addition to present extensive numerical simulations, we provide some rigorous results. Finally, I briefly introduce some recent works on this topic.