报告题目：Ensemble inference of unobserved infections in networks using partial observations
报 告 人：张仁权 副教授 必赢3003am
报告时间：2023年11月9日（星期四） 11：10-11:40
报告地点：海山楼（创新园大厦）A1101
校内联系人：张仁权 副教授 联系电话：84708351-8313
报告摘要：Undetected infections fuel the dissemination of many infectious agents. However, identifying unobserved infectious individuals remains challenging due to limited observations of infections and imperfect knowledge of key transmission parameters. Here, we use an ensemble Bayesian inference method to infer unobserved infections using partial observations. The ensemble inference method can represent uncertainty in model parameters and update model states using all ensemble members collectively. We perform extensive experiments in model-generated and real-world networks in which individuals have differential but unknown transmission rates. The ensemble method outperforms several alternative approaches for a variety of network structures and observation rates, even though the model is misspecified. The inference method can potentially support decision-making under uncertainty and be adapted for use in other dynamical models in networks.
报告人简介：张仁权，副教授，硕士生导师，长期从事复杂网络、数据同化、传染病推断与预测方面的研究，主持和参与国家科技部重点研发计划、国家自然科学基金面上和青年项目、辽宁省自然科学基金面上项目，博士后基金和横向课题等科研项目10余项，在PLoS Computational Biology、Physica D、Physical Review E、Chaos等期刊上发表高水平学术论文近20篇。入选大连市高层次人才创新支持计划（青年科技之星）、必赢3003am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。
