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.
1] Department of Public Health Sciences. Karolinska Institutet, Stockholm, Sweden [2] Flowminder Foundation, Stockholm, Sweden.
Linus
L
Bengtsson
Aix-Marseille University, UMR 912 SESSTIM (INSERM-IRD-AMU), Marseille, France.
Jean
J
Gaudart
1] College of Information System and Management, National University of Defence Technology, Changsha, China [2] Department of Public Health Sciences. Karolinska Institutet, Stockholm, Sweden [3] Flowminder Foundation, Stockholm, Sweden.
Xin
X
Lu
Aix-Marseille University, UMR MD 3, Marseille, France.
Sandra
S
Moore
1] Flowminder Foundation, Stockholm, Sweden [2] Stockholm School of Economics, Stockholm, Sweden.
Erik
E
Wetter
Aix-Marseille University, UMR 912 SESSTIM (INSERM-IRD-AMU), Marseille, France.
Kankoe
K
Sallah
Aix-Marseille University, UMR MD 3, Marseille, France.
Stanislas
S
Rebaudet
Aix-Marseille University, UMR MD 3, Marseille, France.
Renaud
R
Piarroux
D040421:Cell Phone / Q000706:statistics & numerical data*; D002771:Cholera / Q000453:epidemiology*; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D040362:Geographic Information Systems / Q000706:statistics & numerical data; D062305:Geographic Mapping; D006205:Haiti / Q000453:epidemiology; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis