Computational Epidemiology on Social Networks

Summary:

In this project, a team of physicists, mathematicians, computer and decision scientists work on the analysis of the data generated by a large-scale epidemics simulation EPISIMS (Los Alamos National Laboratory Epidemiological Simulation). EPISIMS gives information on the whereabouts of 1.5 million people in the city of Portland, Oregon based on census, land-usage and survey data invaluable to the study of social processes and epidemiology. This is a huge data set, so we had to come up with new methods such as using a large-scale SQL database system to analyze it. Many mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. In this project, we developed 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. Within this large-scale simulation framework, we also analyze the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggested that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population. 

Publications:

1. Modeling Disease Outbreaks in Realistic Urban Social Networks, S. Eubank, H. Guclu, V.S.A. Kumar, M. Marathe, A. Srinivasan, Z. Toroczkai and N. Wang, Nature 429, 180 (2004).
   
   
2. Dynamic Proximity Networks, Z. Toroczkai and H. Guclu, Physica A 378, 68 (2007).

Links:

• EPISIMS Homepage
• Nature Supplementary Information