報告題目：Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection

報告時間：2021年11月12日（周五）下午15:30

報告地點：騰訊會議997805634

報告人：鄒秀芬教授

報告人單位：武漢大學

報告人簡介：

鄒秀芬, 武漢大學數(shù)學與統(tǒng)計學院二級教授，博士生導師，中國工業(yè)與應用數(shù)學學會數(shù)學生命科學專業(yè)委員會副主任，中國運籌學會計算系統(tǒng)生物學常務理事，長期從事數(shù)學與生物醫(yī)學等交叉學科研究。近年來主持承擔了國家自然科學基金重點項目、面上項目和科技部國家重大研究計劃課題等科研課題。在癌癥等復雜疾病的海量數(shù)據(jù)集成、多尺度建模和復雜疾病的優(yōu)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”等國際重要學術期刊上發(fā)表相關的學術論文。

報告摘要：

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.

邀請單位：數(shù)學與統(tǒng)計學院