报告题目：Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection

报告时间：2021年11月12日（周五）下午15:30

报告地点：腾讯会议997805634

报告人：邹秀芬教授

报告人单位：武汉大学

报告人简介：

邹秀芬, 武汉大学数学与统计学院二级教授，博士生导师，中国工业与应用数学学会数学生命科学专业委员会副主任，中国运筹学会计算系统生物学常务理事，长期从事数学与生物医学等交叉学科研究。近年来主持承担了国家自然科学基金重点项目、面上项目和科技部国家重大研究计划课题等科研课题。在癌症等复杂疾病的海量数据集成、多尺度建模和复杂疾病的优化控制等方面取得了一系列成果，已在“PNAS”，“SIAM on Applied Mathematics”, “Applied Mathematical Modeling”, “PLOS Computational biology”, “Bulletin of Mathematical Biology”, “IEEE Transactions on Biomedical Engineering”等国际重要学术期刊上发表相关的学术论文。

报告摘要：

Based on available data for COVID-19, we presented two mathematical models for SARS-CoV-2 infection. One is the coinfection of SARS-CoV-2 and bacteria to investigate the dynamics of COVID-19 progress. Another is a multi-scale computational model to understand the heterogeneous progression of COVID-19 patients. Combining theoretical analysis, numerical simulations and quantitative computations, we revealed that initial bacterial infection and immune-related parameters have great influences on the severity degree and mortality in COVID-19 patients. We further identified that T cell exhaustion plays a key role in the transition between mild-moderate and severe symptoms. In addition, we quantified the efficacy of treating COVID-19 patients and investigated the effects of various therapeutic strategies. These results highlight the critical roles of IFN and T cell responses in regulating the stage transition during COVID-19 progression.

邀请单位：数学与统计学院