Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data

Abstract

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.

Keywords: Big Data; human mobility; mobile phones; spatial epidemiology.

Figure 1.

A schematic describing the spatial scales of mobility measured using mobile phone calling data (map, above) and indicating what associated infectious disease data might look like (time course, below). Using mobile phone CDRs, an individual subscriber's location can be geolocated on the tower level (far right); however, this may be difficult to use in conjunction with the locations and timings of individual cases, given highly sporadic incidence. These mobility and infectious disease data can be aggregated to larger spatial areas, such as those between administrative units (middle panel), where patterns of incidence may resolve into clearer outbreaks, as, for example, when lags between outbreaks might map onto the flow of many individuals between larger spatial units.