Connecting mobility to infectious diseases: The promise and limits of mobile phone data

Amy Wesolowski, Caroline O. Buckee, Kenth Engø-Monsen, C. J.E. Metcalf

Research output: Contribution to journalArticlepeer-review

135 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)S414-S420
JournalJournal of Infectious Diseases
StatePublished - Dec 1 2016

All Science Journal Classification (ASJC) codes

  • Infectious Diseases
  • Immunology and Allergy


  • Big Data
  • Human mobility
  • Mobile phones
  • Spatial epidemiology


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