2018年论坛学术报告

[数学论坛] Combining network theory and partial differential equation to improve influenza predictions
发布日期:2018-06-19  浏览量:

报告题目: Combining network theory and partial differential equation to improve influenza predictions

报告人:王海燕教授(美国亚利桑那州立大学)

报告地点:主321

报告时间:2018621日上午1030-1130

报告摘要: The ever-increasing availability of geospatial data now opens the possibility to use spatio-temporal models to more accurately predict patterns of movement and trends in human activities, epidemic spread, environmental changes and many other natural phenomena. In this talk, we present an integrated framework for early detection of epidemic outbreaks based on real-time geo-tagged data in Twitter.  We combine network theory, data mining and partial differential equation models to describe/predict patterns of epidemic spread at a regional level. In addition, I will discuss a number of mathematical problems including free boundary value problems and bifurcation problems arising from these applications.

报告人简介  王海燕教授为美国亚利桑那州立大学教授,主要从事大数据、生物数学、微分方程、控制及社会网络等方面的研究工作。近几年在大数据建模和社交网络方面取得了一定的研究成果。特别是,最近几年王海燕教授及其团队用在线社交网络的数据和偏微分方程(PDE)研究社交网络的信息传播和传染病动力学.这项工作已经由美国国家科学基金会资助.有关的论文发表在国际顶级期刊(JDE).

邀请人:李翠萍

     

 

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