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

报告时间：2019年01月05日   19:30-21:30

报告地点：数学与统计学院金融实验室

报告人：美国亚利桑那州立大学   王海燕教授

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.