数学系学术报告
微分方程与动力系统系列报告
报告题目：Interplay between COVID-19 vaccines and social measures for ending the SARS-CoV-2 pandemic
报告人：孝鹏程助理教授
报告时间：2021/9/27 09:30-10:30
腾讯会议：5318371238
报告摘要：The development and authorization of COVID-19 vaccines has provided the clearest path forward to eliminate community spread hence end the ongoing SARS-CoV-2 pandemic. However, the limited pace at which the vaccine can be administered motivates the question, to what extent must we continue to adhere to social intervention measures such as mask wearing and social distancing? To address this question, we develop a mathematical model of COVID-19 spread incorporating both vaccine dynamics and socio-epidemiological parameters. We use this model to study two important measures of disease control and eradication, the effective reproductive number and the peak intensive care unit (ICU) caseload, over three key parameters: social measure adherence, vaccination rate, and vaccination coverage. Our results suggest that, due to the slow pace of vaccine administration, social measures must be maintained by a large proportion of the population until a sufficient proportion of the population becomes vaccinated for the pandemic to be eradicated. By contrast, with reduced adherence to social measures, hospital ICU cases will greatly exceed capacity, resulting in increased avoidable loss of life. These findings highlight the complex interplays involved between vaccination and social protective measures, and indicate the practical importance of continuing with extent social measures while vaccines are scaled up to allow the development of the herd immunity needed to end or control SARS-CoV-2 sustainably.
报告人简介：孝鹏程，男，2005年考入中国矿业大学信息安全系，2009年本科毕业留学美国。2011年获美国德克萨斯大学大河谷分校（University of Texas Rio Grande Valley）数学硕士学位。2015年获美国德克萨斯大学阿灵顿分校(University of Texas at Arlington)应用数学博士学位。2015-2019在美国伊凡斯维尔大学(University of Evansville)任职助理教授。2019年至今现任美国肯尼索州立大学（Kennesaw State University）数学系助理教授，硕导，主要研究方向有计算神经学，生物数学和机器学习。