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

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

Wesolowski A¹ Metcalf CJ² Eagle N³ Kombich J⁴ Grenfell BT⁵ Bjørnstad ON⁶ Lessler J⁷ Tatem AJ⁸ Buckee CO⁹.

Author information

• ¹Department of Epidemiology Harvard School of Public Health Boston MA 02115; Center for Communicable Disease Dynamics Harvard School of Public Health Boston MA 02115; Flowminder Foundation SE-113 55 Stockholm Sweden; awesolow@hsph.harvard.edu cmetcalf@princeton.edu.
• ²Department of Ecology and Evolutionary Biology Princeton University Princeton NJ 08544; Woodrow Wilson School Princeton University Princeton NJ 08544; Fogarty International Center National Institute of Health Bethesda MD 20892; awesolow@hsph.harvard.edu cmetcalf@princeton.edu.
• ³Department of Epidemiology Harvard School of Public Health Boston MA 02115; Department of Computer Science Northeastern University Boston MA 02115;
• ⁴Department of Biological Sciences University of Kabianga Kericho Country Kenya;
• ⁵Department of Ecology and Evolutionary Biology Princeton University Princeton NJ 08544; Woodrow Wilson School Princeton University Princeton NJ 08544;
• ⁶Center for Infectious Disease Dynamics The Pennsylvania State University State College PA 16801;
• ⁷Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205;
• ⁸Flowminder Foundation SE-113 55 Stockholm Sweden; Fogarty International Center National Institute of Health Bethesda MD 20892; Department of Geography and Environment University of Southampton Southampton SO17 1BJ United Kingdom.
• ⁹Department of Epidemiology Harvard School of Public Health Boston MA 02115; Center for Communicable Disease Dynamics Harvard School of Public Health Boston MA 02115; Flowminder Foundation SE-113 55 Stockholm Sweden;

Charts and tables on http://www.ncbi.nlm.nih.gov/pubmed/25643101

Amina I

Abstract

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 cross-sectional 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.