学术报告报告题目：Applying “dynamic model + deep learning” to examining the efficacy of interventions during COVID-19 pandemic
报 告 人：肖燕妮教授（西安交通大学数学与统计学院）
报告时间：2024年5月23日（星期四） 10:00-11:00
报告地点：37号楼 3A01报告厅
邀 请 人：刘锐教授、陈培教授
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数学学院
2024年5 月11日
报告摘要：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 Jianhong Wu, Pengfei Song, Mengqi He and Sanyi Tang.
报告人简介：肖燕妮教授现任西安交通大学数学与统计学院副院长、数学与生命科学交叉研究中心主任、博士生导师，主要从事非光滑动力学系统理论及应用研究、数据和问题驱动的传染病动力学研究，主要成果发表在JDE, J Math Biol, Bull Math Biol, PLoS Comput Biol, BMC Medicine等著名国际期刊上。与中国疾病预防控制中心合作完成了国家“十一五”、“十二五”和“十三五”科技重大专项艾滋病领域的建模研究、合作了基于模型和数据的新冠疫情预测预警、最优解封策略等研究。 2022年起任中国生物数学专业委员会主任，2020年起任国务院第八届学科评议组成员（数学），2020年获陕西最美科技工作者。