基于人类集群行为的复杂网络流行病动力学研究

项目名称： 基于人类集群行为的复杂网络流行病动力学研究

项目编号： No.61273223

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 李翔

作者单位： 复旦大学

项目金额： 80万元

中文摘要： 本项目立足于复杂网络流行病传染模型与动力学演化，综合复合种群与主体模型，围绕人类集群行为的长途迁移、近距离时空接触和个体自适应决策三个方面，研究基于人类集群行为的复杂网络流行病动力学演化、预测与控制，重视基于实际航空流量与人群迁移模式双重异质性的复合种群传染临界条件和标度演化解析，分析基于个体近距离时空接触和长途迁移模式下流行病临界阈与人类集群行为的相互影响，剖析个体自适应决策与复杂时效网络流行病动力学共演机制等关键问题，以期能预测大范围疫情的流行并提出有效控制对策。 SARS曾经震动世界，甲流又再次席卷全球。本项目将力争厘清人类集群的接触/迁移等行为模式与复杂网络流行病动力学的内在联系，融合个体自适应决策博弈机制，完善流行病传染临界理论和动力学分析与控制的体系，推动对现实社会流行病传播与动力学演化机制的理解，为可靠预测和有效控制（全球性）大规模疫情的爆发与蔓延提供理论依据和方法指导。

中文关键词： 复杂种群网络；流行病演化；人类集群动力学；自适应决策；疫情入侵推断与预测

英文摘要： This project aims at the study of human-population-behavior-driven epidemic evolution, prediction, and control on complex networks, which, with the basis of complex network infectious models and dynamics evolution, synthesizes the meta-population and agent-based models, and focus on the three main aspects of human population behaviors: long distance travel, close spatial-temporal contact, and individual adaptive decision. In this four-year program, to predict the prevalence of infections with effective countermeasures, we will devoted ourselves to finding the answers of several key problems, including (1) the infectious threshold analyses of meta-populations as well as the scaling evolution with the heterogeneity in both the true air traffic and human migration patterns, (2) the interdependence between infectious epidemic thresholds and human population behaviors with the impact from close spatial temporal contact and long distance travel, (3) the coevolution of individual adaptive decisions and temporal network epidemic dynamics. SARS shocked the whole world, and the A-H1N1 has prevailed globally again. This project targets to disentangle the dense interactions among complex network epidemic dynamics and human population migration/contact patterns, and, with the mechanisms of individual adaptiv

英文关键词： Complex metapopulation networks；epidemic evolution；human collective dynamics；adatpvie decision；epidemic invasion reference and prediction