天元动力系统系列报告

报告题目: Optimal Reopening Pathways with COVID-19 Vaccine Rollout and Emerging Variants of Concern

报告人：吴建宏  加拿大约克大学工业与应用数学实验室

报告时间：2022年5月27日（星期五）上午9:00-11:00

报告地点：腾讯会议在线： 888-864-612     密码：123456

报告摘要： We developed a stochastic optimization technology based on a COVID-19 transmission dynamics model to determine optimal pathways from lockdown toward reopening with different scales and speeds of mass vaccine rollout in order to maximize social economical activities while not overwhelming the health system capacity. We used the Province of Ontario, Canada as a case study to demonstrate the methodology and the optimal decision trees; but our method and algorithm are generic and can be adapted to other settings. Our model framework and optimization strategies take into account the likely range of social contacts during different phases of a gradual reopening process and consider the uncertainties of these contact rates due to variations of individual behaviors and compliance. The results show that optimal pathways toward near pre-pandemic activity level is feasible given an accelerated vaccination rollout plan, with the final activity level being determined by the vaccine coverage and the transmissibility of the dominating strain.

报告人简介： 吴建宏，加拿大约克大学终身教授，应用数学首席教授，博士生导师。加拿大首席资深工业与应用教学研究主席、约克大学工业与应用数学实验室主任、加拿大应用与工业数学学会主席、加拿大约克大学现代危机与灾难快速反应模拟中心主任。同时兼任本学科领域世界顶级期刊杂志編委：担任菲尔兹研究所委托的加拿大国家COVID-19大流行快速响应建模工作组的负责人。在Kluwer、AMS/Fields、Springer、Wiley等出版社出版专著8 部，编著14 部，发表学术论文500多篇。研究方向包括：动力系统、神经网络和模式识别、生物数学和流行病学。获加拿大女王钻石禧功勋章、约克大学终身杰出特聘教授、终身Fields Institute Fellow、加拿大华人专业人士杰出成就奖、加拿大新先锋科技奖等在内的10余项学术成就奖。