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(diǎn):騰訊會議997805634
報告人:鄒秀芬教授
報告人單位:武漢大學(xué)
報告人簡介:
鄒秀芬, 武漢大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院二級教授,博士生導(dǎo)師,中國工業(yè)與應(yīng)用數(shù)學(xué)學(xué)會數(shù)學(xué)生命科學(xué)專業(yè)委員會副主任,中國運(yùn)籌學(xué)會計(jì)算系統(tǒng)生物學(xué)常務(wù)理事,長期從事數(shù)學(xué)與生物醫(yī)學(xué)等交叉學(xué)科研究。近年來主持承擔(dān)了國家自然科學(xué)基金重點(diǎn)項(xiàng)目、面上項(xiàng)目和科技部國家重大研究計(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”等國際重要學(xué)術(shù)期刊上發(fā)表相關(guān)的學(xué)術(shù)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(xué)與統(tǒng)計(jì)學(xué)院