ArticleScientific reports2016
Unveiling Spatial Epidemiology of HIV with Mobile Phone Data.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Modifiable Areal Unit Problems for Infectious Disease Cases Described in Medicare and Medicaid Claims, 2016-2019.Journal of bacteriology & parasitology · 2024Article
- Article
- Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review.Current HIV/AIDS reports · 2023Article
- Call detail record aggregation methodology impacts infectious disease models informed by human mobility.PLoS computational biology · 2023Article
- Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action.International journal of environmental research and public health · 2023Article
- Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach.International journal of public health · 2023Article
- Monitoring supply networks from mobile phone data for estimating the systemic risk of an economy.Scientific reports · 2022Article
- Analysis of mHealth research: mapping the relationship between mobile apps technology and healthcare during COVID-19 outbreak.Globalization and health · 2022Review
- Population data mobility retrieval at territory of Czechia in pandemic COVID-19 period.Concurrency and computation : practice & experience · 2021Article
- Using big data for evaluating development outcomes: A systematic map.Campbell systematic reviews · 2021Article
- Review
- Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia.Nature communications · 2021Article
- Ensemble model for estimating continental-scale patterns of human movement: a case study of Australia.Scientific reports · 2021Article
- Where Are the Newly Diagnosed HIV Positives in Kenya? Time to Consider Geo-Spatially Guided Targeting at a Finer Scale to Reach the "First 90".Frontiers in public health · 2021Article
- Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020.Globalization and health · 2020Article
- A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa.International journal of environmental research and public health · 2020Article
- An overview of GeoAI applications in health and healthcare.International journal of health geographics · 2019Article
- Geographic Information Systems, spatial analysis, and HIV in Africa: A scoping review.PloS one · 2019Article
- Bringing Real-Time Geospatial Precision to HIV Surveillance Through Smartphones: Feasibility Study.JMIR public health and surveillance · 2018Article
- Geospatial modelling in guiding health program strategies in resource-limited settings-the way forward.Annals of translational medicine · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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