Data-Driven Methods for Modeling, Prediction, and Inference in Infectious Diseases

：张仁权（大连理工大学）
：2025-08-28 11:00
：海韵园实验楼S106

报告人：张仁权（大连理工大学）

时  间：2025年8月28日11:00

地  点：海韵园实验楼S106

内容摘要:

In this talk, we analyze aggregated foot traffic data derived from mobile devices to measure the connectivity among 42 NYC neighborhoods driven by various human activities such as dining, shopping, and entertainment. Using real-world time-varying contact patterns in different place categories, we develop a parsimonious behavior-driven epidemic model that incorporates population mixing, indoor crowdedness, dwell time, and seasonality of virus transmissibility. We fit this model to neighborhood-level COVID-19 case data in NYC and further couple this model with a data assimilation algorithm to generate short-term forecasts of neighborhood-level COVID-19 cases in 2020. We find differential contact patterns and connectivity between neighborhoods driven by different human activities. The behavior-driven model supports accurate modeling of neighborhood-level SARS-CoV-2 transmission throughout 2020.

个人简介：

张仁权，大连理工大学数学科学学院副教授，副院长，长期从事复杂网络与传染病预测方面的研究，主持和参与国家自然科学基金面上和青年项目、国家科技部重点研发计划、JKW重大专项重点项目、辽宁省自然科学基金面上项目等科研项目10余项，在PLos Computational Biology、Information Sciences、Physica D、Physical Review E、Chaos等期刊上发表高水平学术论文20余篇。入选大连市高层次人才创新支持计划（青年科技之星）、大连理工大学星海人才培育计划（星海骨干），获得辽宁省自然科学学术成果二等奖。

 

联系人：黄文