Using Mobile Phone Data to Predict the Spatial Spread of Cholera

Abstract

Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.

Crisis analytics: big data-driven crisis response

“…CDRs have been used by digital humanitarians during various crises (such as the non-profit FlowMinder's (Flowminder) work with anonymous mobile operator data during the Haiti earthquake to follow the massive population displacements) to not only point out the current locations of populations, but also predict their future trajectory (Bengtsson et al 2011). FlowMinder used the same mobile analytics methodology during the cholera epidemic later that year to enable rapid monitoring of population movement from the outbreak area (Bengtsson et al 2015).…”

Causal mechanisms in organization and innovation studies

“…Both secondary data, such as those generated by emails, cameras and positioning systems, as well as primary data collected by researchers to address a specific set of question(s) can provide data streams related to actions and interactions such as individuals' movements and communication with one another (Bengtsson et al, 2015). New technologies facilitate the integration of new types of data (e.g.…”

Sources of VGI for Mapping

“…These data have been analysed to investigate applications in areas such as transportation planning (Di Lorenzo et al, 2016), user behaviour (Bianchi et al, 2016), public health (Oliver et al, 2015), the spatial spread of diseases such as cholera (Bengtsson et al, 2015) or population displacement after a major disaster (Wilson et al, 2016).…”