期刊
JOURNAL OF INFECTIOUS DISEASES
卷 214, 期 -, 页码 S414-S420出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/infdis/jiw273
关键词
spatial epidemiology; Big Data; mobile phones; human mobility
资金
- James S. McDonnell Foundation
- Wellcome Trust [106866/Z/15/Z]
- Bill and Melinda Gates Foundation
- Models of Infectious Disease Agent Study program [1U54GM088558]
Human travel can shape infectious disease dynamics by introducing pathogens into susceptible populations or by changing the frequency of contacts between infected and susceptible individuals. Quantifying infectious disease-relevant travel patterns on fine spatial and temporal scales has historically been limited by data availability. The recent emergence of mobile phone calling data and associated locational information means that we can now trace fine scale movement across large numbers of individuals. However, these data necessarily reflect a biased sample of individuals across communities and are generally aggregated for both ethical and pragmatic reasons that may further obscure the nuance of individual and spatial heterogeneities. Additionally, as a general rule, the mobile phone data are not linked to demographic or social identifiers, or to information about the disease status of individual subscribers (although these may be made available in smaller-scale specific cases). Combining data on human movement from mobile phone data-derived population fluxes with data on disease incidence requires approaches that can tackle varying spatial and temporal resolutions of each data source and generate inference about dynamics on scales relevant to both pathogen biology and human ecology. Here, we review the opportunities and challenges of these novel data streams, illustrating our examples with analyses of 2 different pathogens in Kenya, and conclude by outlining core directions for future research.
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