报告题目：Combining network theory and partial differential equation to improve influenza prediction
报 告 人：王海燕 教授
报告地点：磬苑校区数学楼H306报告厅
报告时间：5月15日（周三）上午: 10:30-11:30
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.
欢迎各位老师、同学届时前往！
科学技术处
2019年5月13日