Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 112, Issue 35, Pages 11114-11119Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1423542112
Keywords
rubella; mobile phones; population mobility; Kenya; seasonality
Categories
Funding
- National Science Foundation [0750271]
- James S. McDonnell Foundation
- Bill and Melinda Gates Foundation [49446, OPP1032350]
- National Institutes of Health (NIH)/National Institute of Allergy and Infectious Disease [U19AI089674]
- Research and Policy for Infectious Disease Dynamics (RAPIDD) program of the Science and Technology Directorate, Department of Homeland Security
- Fogarty International Center, NIH
- Models of Infectious Disease Agent Study program [1U54GM088558]
- Bill and Melinda Gates Foundation
- Science and Technology Directorate, Department of Homeland Security [HSHQDC-12-C-00058]
- RAPIDD program of the Science and Technology Directorate, Department of Homeland Security
- Wellcome Trust [106866/Z/15/Z]
- Direct For Education and Human Resources
- Division Of Graduate Education [0750271] Funding Source: National Science Foundation
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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.
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