Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2015-09, Vol.112 (35), p.11114-11119
Main Authors: Wesolowski, Amy, Metcalf, C. J. E., Eagle, Nathan, Kombich, Janeth, Grenfell, Bryan T., Bjørnstad, Ottar N., Lessler, Justin, Tatem, Andrew J., Buckee, Caroline O.
Format: Article
Language:English
Subjects:
Biological Sciences
Cell Phone
Data Interpretation, Statistical
Density
Disease transmission
Humans
Measles
Rubella - transmission
Seasons
Time
Travel
Weather
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Summary:Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or crosssectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1423542112