Forecasting Epidemics: Combining Networks and Partial Differential Equations to Improve Predictions

Haiyan Wang uses geo-data from Twitter and mathematical modeling to better predict epidemic spread regionally

The ever-increasing availability of geospatial data 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.

ASU Professor Haiyang Wang will discuss an integrated framework for early detection of epidemic outbreaks based on real-time geo-tagged data in Twitter. The approach combines network theory, data mining and partial differential equation models to describe and predict patterns of epidemic spread at regional levels. He will also discuss free boundary value problems and bifurcation problems arising from these applications.

Wang’s research interests are in applied mathematics, differential equations, mathematical biology, online social networks and big data. Wang, professor in the School of Mathematical and Natural Sciences in ASU’s New College of Interdisciplinary Arts and Sciences, joined ASU’s West campus as an assistant professor in 2005.

He earned a doctorate in mathematics and a master’s in computer science simultaneously at Michigan State University. Wang also holds a master’s in applied mathematics, from Ocean University of China.

This event is part of the Science and Mathematics Colloquium Series at ASU's Polytechnic campus.

Faculty of Science and Mathematics, College of Integrative Sciences and Arts
Steven Saul
Wed., March 21, 3 p.m.
Student Union, Cooley Ballroom C
Polytechnic campus

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