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-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.
Department 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.
Amy
A
Wesolowski
Department 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.
C J E
CJ
Metcalf
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115; Department of Computer Science, Northeastern University, Boston, MA 02115;
Nathan
N
Eagle
Department of Biological Sciences, University of Kabianga, Kericho Country, Kenya;
Janeth
J
Kombich
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544; Woodrow Wilson School, Princeton University, Princeton, NJ 08544;
Bryan T
BT
Grenfell
Center for Infectious Disease Dynamics, The Pennsylvania State University, State College, PA 16801;
Ottar N
ON
Bjørnstad
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;
Justin
J
Lessler
Flowminder 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.
Andrew J
AJ
Tatem
Department 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;
Caroline O
CO
Buckee
D040421:Cell Phone; D003627:Data Interpretation, Statistical; D006801:Humans; D012409:Rubella / Q000635:transmission*; D012621:Seasons
Kenya; mobile phones; population mobility; rubella; seasonality