Proceedings of the Royal Society B: Biological Sciences
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Human mobility patterns predict divergent epidemic dynamics among cities

Benjamin D. Dalziel

Benjamin D. Dalziel

Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA

[email protected]

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,
Babak Pourbohloul

Babak Pourbohloul

Division of Mathematical Modeling, British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada

School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada

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and
Stephen P. Ellner

Stephen P. Ellner

Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA

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Published:https://doi.org/10.1098/rspb.2013.0763

    The epidemic dynamics of infectious diseases vary among cities, but it is unclear how this is caused by patterns of infectious contact among individuals. Here, we ask whether systematic differences in human mobility patterns are sufficient to cause inter-city variation in epidemic dynamics for infectious diseases spread by casual contact between hosts. We analyse census data on the mobility patterns of every full-time worker in 48 Canadian cities, finding a power-law relationship between population size and the level of organization in mobility patterns, where in larger cities, a greater fraction of workers travel to work in a few focal locations. Similarly sized cities also vary in the level of organization in their mobility patterns, equivalent on average to the variation expected from a 2.64-fold change in population size. Systematic variation in mobility patterns is sufficient to cause significant differences among cities in infectious disease dynamics—even among cities of the same size—according to an individual-based model of airborne pathogen transmission parametrized with the mobility data. This suggests that differences among cities in host contact patterns are sufficient to drive differences in infectious disease dynamics and provides a framework for testing the effects of host mobility patterns in city-level disease data.

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