Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations

Details Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations

2007-06-25

arxiv.org/abs/0706.3647v2

Journal of Theoretical Biology 251, 450-467 (2008)

Biology

Quantitative Biology

Populations and Evolution

18 pages, 8 figures, minor corrections, inclusion of subleading terms

Scientific paper

The spatial structure of populations is a key element in the understanding of the large scale spreading of epidemics. Motivated by the recent empirical evidence on the heterogeneous properties of transportation and commuting patterns among urban areas, we present a thorough analysis of the behavior of infectious diseases in metapopulation models characterized by heterogeneous connectivity and mobility patterns. We derive the basic reaction-diffusion equation describing the metapopulation system at the mechanistic level and derive an early stage dynamics approximation for the subpopulation invasion dynamics. The analytical description uses degree block variables that allows us to take into account arbitrary degree distribution of the metapopulation network. We show that along with the usual single population epidemic threshold the metapopulation network exhibits a global threshold for the subpopulation invasion. We find an explicit analytic expression for the invasion threshold that determines the minimum number of individuals traveling among subpopulations in order to have the infection of a macroscopic number of subpopulations. The invasion threshold is a function of factors such as the basic reproductive number, the infectious period and the mobility process and it is found to decrease for increasing network heterogeneity. We provide extensive mechanistic numerical Monte Carlo simulations that recover the analytical finding in a wide range of metapopulation network connectivity patterns. The results can be useful in the understanding of recent data driven computational approaches to disease spreading in large transportation networks and the effect of containment measures such as travel restrictions.

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