Modelling disease outbreaks in realistic urban social networks

Stephen Eubank (), Hasan Guclu, V. S. Anil Kumar, Madhav V. Marathe, Aravind Srinivasan, Zoltán Toroczkai and Nan Wang
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Stephen Eubank: Los Alamos National Laboratory, MS M997
Hasan Guclu: Rensselaer Polytechnic Institute
V. S. Anil Kumar: Los Alamos National Laboratory, MS M997
Madhav V. Marathe: Los Alamos National Laboratory, MS M997
Aravind Srinivasan: University of Maryland
Zoltán Toroczkai: Los Alamos National Laboratory, MS B258
Nan Wang: University of Maryland

Nature, 2004, vol. 429, issue 6988, 180-184

Abstract: Abstract Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions1 or ad hoc models for the contact process2,3,4. 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.

Date: 2004
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DOI: 10.1038/nature02541

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