报告地点：行健楼学术活动室665
邀请人：万辉教授
摘要：During the COVID-19 pandemic, control measures play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. In this talk, we initially estimate the effective reproduction number by universal differential equation method which embeds neural network into a differential equation. We then develop the mechanism of physical-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models, to precisely quantify the intensity of interventions. The selected rate functions, quantifying the intensity of interventions, based on the time series inferred by deep learning have epidemiologically reasonable meanings. Finally I shall give some concluding remarks. This is a joint work with Pengfei Song, Mengqi He and Sanyi Tang.
报告人简介：肖燕妮，教授，博士生导师，国务院学位委员会第八届学科评议组成员，中国数学会生物数学专业委员会主任，陕西科技创新领军人才，西安交通大学数学与生命科学交叉研究中心主任，2001年在中国科学院数学与系统科学研究院数学所获得理学博士学位，毕业时获“中国科学院院长优秀奖”。 2003年1月至2006年5月在英国Liverpool大学数学系和兽医系做博士后研究。2006年6月被聘为西安交通大学理学院教授。2008入选教育部新世纪优秀人才支持计划。主要从事非光滑动力学模型、 生物医学大数据分析、新发重大传染病的预测预警等方面的研究，主持十一五国家科技重大专项课题、国家自然科学基金等项目多项。
