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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Metcalf, C. J. E. Kombich, Janeth Lessler, Justin Wesolowski, Amy Buckee, Caroline O. Bjørnstad, Ottar N. Tatem, Andrew J. Grenfell, Bryan T. Eagle, Nathan |
| Description | Author Affiliation: Wesolowski A ( Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115); Metcalf CJ ( Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544); Eagle N ( Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115); Kombich J ( Department of Biological Sciences, University of Kabianga, Kericho Country, Kenya); Grenfell BT ( Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544); Bjørnstad ON ( Center for Infectious Disease Dynamics, The Pennsylvania State University, State College, PA 16801); Lessler J ( Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205); Tatem AJ ( Flowminder Foundation, SE-113 55 Stockholm, Sweden); Buckee CO ( Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115); |
| 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. |
| ISSN | 00278424 |
| e-ISSN | 10916490 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Issue Number | 35 |
| Volume Number | 112 |
| Language | English |
| Publisher | National Academy of Sciences |
| Publisher Date | 2015-09-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Cell Phones Data Interpretation, Statistical Rubella Transmission Seasons Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Multidisciplinary |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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