时 间：2024年01月13日（周六），8:20-9:50
地 点：腾讯会议：759-517-0738，密码：654321
报告人：唐彪，西安交通大学特聘研究员
摘要：Fluctuations or periodic outbreaks are prevalent phenomena in the dynamics of various infectious diseases. Typically, the examination of the existence and stability of periodic solutions is essential for a comprehensive understanding of epidemic oscillations in the long-term epidemic patterns. In this talk, I will introduce several modeling frameworks and the machine learning-based data-driven approach, to unveil the underlying mechanisms responsible for the generation of periodic oscillations in epidemics of COVID-19 within finite time frames. Our focus extends to identifying key factors that influence the occurrence of multiple epidemic waves. By doing so, we aim to establish a foundation for decision-making processes that can optimize the design of control strategies for major infectious diseases. Through the integration of advanced modeling techniques and data-driven insights, our research endeavors to enhance our grasp of the dynamics of infectious diseases, ultimately contributing to more effective and targeted public health interventions.
个人简介：唐彪，西安交通大学数学与统计学院特聘研究员，博士生导师，中国数学会生物数学专委会委员。2017年9月毕业于西安交通大学，获理学博士学位。博士期间，受国家留学基金委资助，于加拿大多伦多大学菲尔兹研究所联合培养两年。2017.10-2020.09，在加拿大多伦多大学公共卫生学院和约克大学数学与统计学院从事博士后研究工作。研究领域是生物数学，关注医学、公共卫生与数学交叉的热点问题，在SIAM J Appl Math、Bull Math Biol、J Theoret Biol、Math Biosci、Int J Bifurcat Chao、Vaccine、Appl Math Model等国际知名期刊发表SCI论文40余篇，谷歌学术总引用4100余次，2篇ESI高被引论文和1篇ESI热点论文。主持国家自然科学基金青年项目和面上项目各1项，参与重点项目1项，作为骨干成员参加国家重点研发计划项目2项。
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