Modelling disease outbreaks in realistic urban social networks.
Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. 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-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, 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.
Eubank
Stephen
S
Basic and Applied Simulation Science Group, Los Alamos National Laboratory, MS M997, Los Alamos, New Mexico 87545, USA. eubank@lanl.gov
Guclu
Hasan
H
Kumar
V S Anil
VS
Marathe
Madhav V
MV
Srinivasan
Aravind
A
Toroczkai
Zoltán
Z
Wang
Nan
N
D016358:Contact Tracing; D004196:Disease Outbreaks / Q000517:prevention & control* / Q000706:statistics & numerical data; D006801:Humans; D008954:Models, Biological; D012899:Smallpox / Q000175:diagnosis / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission*; D012900:Smallpox Vaccine; D013997:Time Factors; D014504:Urban Health; D014505:Urban Population; D014611:Vaccination / Q000379:methods
D012900:Smallpox Vaccine