时间：2024年4月18日（星期四）16:00-17:00
地点：经管大楼A楼 四楼第二会议室报告厅
主题：如何增强人类社会抵抗传染病的能力？——一种基于复杂网络流行病阈值最大化的针对性免疫策略（How to Enhance Human Society's Resistance to Infectious Diseases?—A Targeted Immunization Strategy Based on Epidemic Threshold Maximization in Complex Networks）
主讲人：杨平乐（上海理工大学管理学院）
简介：杨平乐，上海理工大学，博士，副教授，硕士生导师。主要研究领域包括人工智能、复杂系统、应急管理等。主持国家社会科学基金一般项目1项，主持上海市科技创新行动计划软科学重点项目1项。在国际权威期刊发表论文30余篇，出版专著1部，申请发明专利19项，授权发明专利9项，授权实用新型专利2项，获得软件著作权8项。
Yang Pingle, Ph.D., Associate Professor, and Master Supervisor at University of Shanghai for Science and Technology. His main research areas include artificial intelligence, complex systems, and emergency management. He has led one general project funded by the National Social Science Foundation and one key project in soft science under the Shanghai Science and Technology Innovation Action Plan. He has published over 30 papers in authoritative international journals, authored one monograph, applied for 19 invention patents, of which 9 have been granted, acquired 2 utility model patents, and obtained 8 software copyrights.
摘要：在人类历史长河中，大规模传染病爆发事件屡见不鲜，这些疫情不仅给全球经济和社会稳定造成了严重冲击，更对人类生命健康构成了巨大威胁。因此，深入研究如何有效控制传染病的传播，已成为当今人类社会面临的重要课题。当前，传染病研究主要集中在病原体鉴定与溯源、流行病学调查与预测、疫苗药物研发以及公共卫生管理等多个方面。本研究深受群体免疫现象启发，特别关注在疫苗资源有限的情况下，如何提升人类社会对传染病的抵御能力。本研究将免疫问题转化为一个优化问题，通过构建以流行病阈值为目标函数、基于节点拓扑势和离散粒子群搜索的群体智能优化免疫策略，旨在实现疫苗接种效益的最大化，显著增强社会网络对传染病的抵抗能力。
In the course of human history, large-scale outbreaks of infectious diseases have been frequent occurrences. These epidemics have not only caused serious impacts on the global economy and social stability but also posed enormous threats to human health and life. Therefore, in-depth research on how to effectively control the spread of infectious diseases has become an important topic facing human society today. Currently, research on infectious diseases mainly focuses on pathogen identification and tracing, epidemiological investigation and prediction, vaccine and drug development, and public health management. This study, inspired by the phenomenon of herd immunity, pays special attention to how to enhance the resistance of human society to infectious diseases in the context of limited vaccine resources. This study transforms the immunity problem into an optimization problem and aims to maximize the effectiveness of vaccination by constructing a Swarm Intelligent Optimization immunization strategy based on the epidemic threshold as the objective function, node topological potential, and discrete particle swarm search. This strategy significantly enhances the resistance of social networks to infectious diseases.
