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Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia

Twenty-six million people are living with HIV in sub-Saharan Africa; epidemics are widely dispersed, due to high levels of mobility. However, global elimination strategies do not consider mobility. We use Call Detail Records from 9 billion calls/texts to model mobility in Namibia; we quantify the ep...

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Autores principales: Valdano, Eugenio, Okano, Justin T., Colizza, Vittoria, Mitonga, Honore K., Blower, Sally
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121904/
https://www.ncbi.nlm.nih.gov/pubmed/33990578
http://dx.doi.org/10.1038/s41467-021-23051-w
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author Valdano, Eugenio
Okano, Justin T.
Colizza, Vittoria
Mitonga, Honore K.
Blower, Sally
author_facet Valdano, Eugenio
Okano, Justin T.
Colizza, Vittoria
Mitonga, Honore K.
Blower, Sally
author_sort Valdano, Eugenio
collection PubMed
description Twenty-six million people are living with HIV in sub-Saharan Africa; epidemics are widely dispersed, due to high levels of mobility. However, global elimination strategies do not consider mobility. We use Call Detail Records from 9 billion calls/texts to model mobility in Namibia; we quantify the epidemic-level impact by using a mathematical framework based on spatial networks. We find complex networks of risk flows dispersed risk countrywide: increasing the risk of acquiring HIV in some areas, decreasing it in others. Overall, 40% of risk was mobility-driven. Networks contained multiple risk hubs. All constituencies (administrative units) imported and exported risk, to varying degrees. A few exported very high levels of risk: their residents infected many residents of other constituencies. Notably, prevalence in the constituency exporting the most risk was below average. Large-scale networks of mobility-driven risk flows underlie generalized HIV epidemics in sub-Saharan Africa. In order to eliminate HIV, it is likely to become increasingly important to implement innovative control strategies that focus on disrupting risk flows.
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spelling pubmed-81219042021-05-18 Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia Valdano, Eugenio Okano, Justin T. Colizza, Vittoria Mitonga, Honore K. Blower, Sally Nat Commun Article Twenty-six million people are living with HIV in sub-Saharan Africa; epidemics are widely dispersed, due to high levels of mobility. However, global elimination strategies do not consider mobility. We use Call Detail Records from 9 billion calls/texts to model mobility in Namibia; we quantify the epidemic-level impact by using a mathematical framework based on spatial networks. We find complex networks of risk flows dispersed risk countrywide: increasing the risk of acquiring HIV in some areas, decreasing it in others. Overall, 40% of risk was mobility-driven. Networks contained multiple risk hubs. All constituencies (administrative units) imported and exported risk, to varying degrees. A few exported very high levels of risk: their residents infected many residents of other constituencies. Notably, prevalence in the constituency exporting the most risk was below average. Large-scale networks of mobility-driven risk flows underlie generalized HIV epidemics in sub-Saharan Africa. In order to eliminate HIV, it is likely to become increasingly important to implement innovative control strategies that focus on disrupting risk flows. Nature Publishing Group UK 2021-05-14 /pmc/articles/PMC8121904/ /pubmed/33990578 http://dx.doi.org/10.1038/s41467-021-23051-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Valdano, Eugenio
Okano, Justin T.
Colizza, Vittoria
Mitonga, Honore K.
Blower, Sally
Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia
title Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia
title_full Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia
title_fullStr Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia
title_full_unstemmed Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia
title_short Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia
title_sort using mobile phone data to reveal risk flow networks underlying the hiv epidemic in namibia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121904/
https://www.ncbi.nlm.nih.gov/pubmed/33990578
http://dx.doi.org/10.1038/s41467-021-23051-w
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