| Title | Massive Social Networks and Epidemiology |
| Speaker | Prof. Aravind Srinivasan, University of Maryland |
| Web page | http://www.cs.umd.edu/~srin |
| Time | Wednesday, October 6, 11:00am |
| Location | HNB 100 (Auditorium) |
Abstract
Most mathematical models for the spread of disease use differential
equations based on uniform mixing assumptions or ad hoc models for
the contact process. We explore the use of dynamic bipartite
graphs to model the physical contact patterns that result from
movements of individuals between specific locations. The graphs are
generated by large-scale individual-based urban traffic simulations
built on actual census, land-use, and population-mobility data. We
find that the contact network among people is a strongly connected
small-world-like graph, and present provably-good algorithms and
their empirical performance for outbreak detection by placing sensors.
Within this large-scale simulation framework, we then analyze the
relative merits of a number of proposed mitigation strategies for
disease-spread.
The talk will mostly be based on the following two papers, and will
also briefly touch upon ongoing work.
Relevant Literature
- "Modelling Disease Outbreaks in Realistic Urban Social Networks", by
S. Eubank, H. Guclu, V. S. A. Kumar, M. V. Marathe, A. Srinivasan,
Z. Toroczkai and N. Wang.
Nature, Vol. 429, 180-184, May 2004;
- "Structural and Algorithmic Aspects of Massive Social Networks", by
S. Eubank, V. S. A. Kumar, M. V. Marathe, A. Srinivasan, and N. Wang.
Proc. ACM-SIAM Symposium on Discrete Algorithms (SODA), 711-720, 2004.