Using Mobile Phone Data to Predict the Spatial Spread of Cholera - Aix-Marseille Université Access content directly
Journal Articles Scientific Reports Year : 2015

Using Mobile Phone Data to Predict the Spatial Spread of Cholera


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.
Fichier principal
Vignette du fichier
srep08923.pdf (469.1 Ko) Télécharger le fichier
Origin Publisher files allowed on an open archive

Dates and versions

hal-01218137 , version 1 (20-10-2015)



Linus Bengtsson, Jean Gaudart, Xin Lu, Sandra Moore, Erik Wetter, et al.. Using Mobile Phone Data to Predict the Spatial Spread of Cholera. Scientific Reports, 2015, 5 (8923), ⟨10.1038/srep08923⟩. ⟨hal-01218137⟩
248 View
336 Download



Gmail Mastodon Facebook X LinkedIn More