Unveiling Spatial Epidemiology of HIV with Mobile Phone Data

TL;DR

This study leverages mobile phone data to analyze and predict the spatial distribution of HIV prevalence in Ivory Coast, identifying key mobility and connectivity factors associated with epidemic hotspots.

Contribution

It introduces a novel approach linking mobile phone data features to HIV spatial variation, providing insights into behavioral patterns influencing epidemic spread.

Findings

Night connectivity and activity are strongly linked to HIV prevalence.

Spatial area covered by users correlates with infection rates.

Communication and mobility hubs align with HIV hot spots.

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 impact the rate of infections. 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.