報(bào)告題目：Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection

報(bào)告時(shí)間：2021年11月12日（周五）下午15:30

報(bào)告地點(diǎn)：騰訊會(huì)議997805634

報(bào)告人：鄒秀芬教授

報(bào)告人單位：武漢大學(xué)

報(bào)告人簡(jiǎn)介：

鄒秀芬, 武漢大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院二級(jí)教授，博士生導(dǎo)師，中國(guó)工業(yè)與應(yīng)用數(shù)學(xué)學(xué)會(huì)數(shù)學(xué)生命科學(xué)專業(yè)委員會(huì)副主任，中國(guó)運(yùn)籌學(xué)會(huì)計(jì)算系統(tǒng)生物學(xué)常務(wù)理事，長(zhǎng)期從事數(shù)學(xué)與生物醫(yī)學(xué)等交叉學(xué)科研究。近年來(lái)主持承擔(dān)了國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目、面上項(xiàng)目和科技部國(guó)家重大研究計(jì)劃課題等科研課題。在癌癥等復(fù)雜疾病的海量數(shù)據(jù)集成、多尺度建模和復(fù)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(guó)際重要學(xué)術(shù)期刊上發(fā)表相關(guān)的學(xué)術(shù)論文。

報(bào)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(qǐng)單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院