Abstract
An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people. With this data, we can extract the knowledge potentially useful for many applications including the one tackled in this study - understanding spatial variation of epidemics. We explored the datasets collected by a cell phone service provider and linked them to spatial HIV prevalence rates estimated from publicly available surveys. For that purpose, 224 features were extracted from mobility and connectivity traces and related to the level of HIV epidemic in 50 Ivory Coast departments. By means of regression models, we evaluated predictive ability of extracted features. Several models predicted HIV prevalence that are highly correlated (>0.7) with actual values. Through contribution analysis we identified key elements that correlate with the rate of infections and could serve as a proxy for epidemic monitoring. Our findings indicate that night connectivity and activity, spatial area covered by users and overall migrations are strongly linked to HIV. By visualizing the communication and mobility flows, we strived to explain the spatial structure of epidemics. We discovered that strong ties and hubs in communication and mobility align with HIV hot spots.
Highlights
An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people
We explored the datasets collected by a cell phone service provider and linked them to spatial HIV prevalence rates estimated from publicly available surveys
In our study we used the Demographic and Health Surveys (DHS) data collected in the Ivory Coast during their 2012 campaign[3]
Summary
An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people With this data, we can extract the knowledge potentially useful for many applications including the one tackled in this study - understanding spatial variation of epidemics. Wesolowski and co-workers explored the impact of the human mobility to the spread of malaria[18] They analyzed CDR data collected by a mobile phone service provider in Kenya over the period of one year and discovered how human mobility patterns contribute to the spread of the disease beyond what could be possible if it was transferred only by insects. Most frequently mentioned are poverty, social instability, violence, high mobility, and rapid urbanization and modernization The differences among these factors could help explain the spatial disparity observed in prevalence rates. The participants were questioned about movement history, and included only two migration destinations, limiting both the extent of usage of study, and the quality of data that was used
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