报告题目：Combining network theory and partial differential equation to improve influenza prediction
报 告 人：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.