1. Basic and Applied Simulation Science Group, Los Alamos National Laboratory, MS M997, Los Alamos, New Mexico 87545, USA
2. Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute 110 8th Street, Troy, New York 12180-3590, USA
3. Department of Computer Science and Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland 20742, USA
4. Centre for Nonlinear Studies and Complex Systems Group, Los Alamos National Laboratory, MS B258, Los Alamos, New Mexico 87545, USA
5. Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA

Correspondence to: Stephen Eubank¹ Correspondence and requests for materials should be addressed to S.G.E. (Email: eubank@lanl.gov).

Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions¹ or ad hoc models for the contact process^{2, 3, 4}. Here 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-like⁵ graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free⁶, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.

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