活動時間：2024-05-24 15:00
活動地點：2号學院樓245
主講人：肖燕妮
主講人中文簡介：
肖燕妮教授，西安交通大學bevictor伟德官网副院長、數學與生命科學交叉研究中心主任、博士生導師，主要從事非光滑動力學系統理論及應用研究、數據和問題驅動的傳染病動力學研究，主要成果發表在JDE,JMath Biol, Bull Math Biol, PLoS Comput Biol, BMC Medicine等著名國際期刊上。與中國疾病預防控制中心合作完成了國家“十一五”、“十二五”和“十三五”科技重大專項艾滋病領域的建模研究、合作了基于模型和數據的新冠疫情預測預警、最優解封策略等研究。 2022年起任中國生物數學專業委員會主任，2020年起任國務院第八屆學科評議組成員(數學)。
活動内容摘要：
Control measures play an important role in mitigating the disease spread during the COVID-19 pandemic, 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.
主持人：李美麗