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

報(bào)告題目：SIR epidemic dynamics in populations with biased mixing: complex network-based approach

報(bào) 告 人：王毅  教授  中國地質(zhì)大學(xué)（武漢）

邀請(qǐng)人：白振國

報(bào)告時(shí)間：2026年1月10日  9：00-10：00

地點(diǎn)：騰訊會(huì)議 637-423-292; 密碼：123456

報(bào)告人簡介：王毅，中國地質(zhì)大學(xué)（武漢）數(shù)學(xué)與物理學(xué)院副院長，教授、博士生導(dǎo)師，主要研究方向?yàn)樯飻?shù)學(xué)與復(fù)雜網(wǎng)絡(luò)。主持國家自然科學(xué)基金3項(xiàng)、湖北省自然科學(xué)基金等項(xiàng)目6項(xiàng)，在BMB、Physica D、Chaos、DCDS-B和JMB等國內(nèi)外期刊發(fā)表論文多篇，合作出版專著3部。

報(bào)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.