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Mathematical models of human mobility of relevance to malaria transmission in Africa

As Africa-wide malaria prevalence declines, an understanding of human movement patterns is essential to inform how best to target interventions. We fitted movement models to trip data from surveys conducted at 3–5 sites throughout each of Mali, Burkina Faso, Zambia and Tanzania. Two models were comp...

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Autores principales: Marshall, John M., Wu, Sean L., Sanchez C., Hector M., Kiware, Samson S., Ndhlovu, Micky, Ouédraogo, André Lin, Touré, Mahamoudou B., Sturrock, Hugh J., Ghani, Azra C., Ferguson, Neil M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5955928/
https://www.ncbi.nlm.nih.gov/pubmed/29769582
http://dx.doi.org/10.1038/s41598-018-26023-1
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author Marshall, John M.
Wu, Sean L.
Sanchez C., Hector M.
Kiware, Samson S.
Ndhlovu, Micky
Ouédraogo, André Lin
Touré, Mahamoudou B.
Sturrock, Hugh J.
Ghani, Azra C.
Ferguson, Neil M.
author_facet Marshall, John M.
Wu, Sean L.
Sanchez C., Hector M.
Kiware, Samson S.
Ndhlovu, Micky
Ouédraogo, André Lin
Touré, Mahamoudou B.
Sturrock, Hugh J.
Ghani, Azra C.
Ferguson, Neil M.
author_sort Marshall, John M.
collection PubMed
description As Africa-wide malaria prevalence declines, an understanding of human movement patterns is essential to inform how best to target interventions. We fitted movement models to trip data from surveys conducted at 3–5 sites throughout each of Mali, Burkina Faso, Zambia and Tanzania. Two models were compared in terms of their ability to predict the observed movement patterns – a gravity model, in which movement rates between pairs of locations increase with population size and decrease with distance, and a radiation model, in which travelers are cumulatively “absorbed” as they move outwards from their origin of travel. The gravity model provided a better fit to the data overall and for travel to large populations, while the radiation model provided a better fit for nearby populations. One strength of the data set was that trips could be categorized according to traveler group – namely, women traveling with children in all survey countries and youth workers in Mali. For gravity models fitted to data specific to these groups, youth workers were found to have a higher travel frequency to large population centers, and women traveling with children a lower frequency. These models may help predict the spatial transmission of malaria parasites and inform strategies to control their spread.
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spelling pubmed-59559282018-05-21 Mathematical models of human mobility of relevance to malaria transmission in Africa Marshall, John M. Wu, Sean L. Sanchez C., Hector M. Kiware, Samson S. Ndhlovu, Micky Ouédraogo, André Lin Touré, Mahamoudou B. Sturrock, Hugh J. Ghani, Azra C. Ferguson, Neil M. Sci Rep Article As Africa-wide malaria prevalence declines, an understanding of human movement patterns is essential to inform how best to target interventions. We fitted movement models to trip data from surveys conducted at 3–5 sites throughout each of Mali, Burkina Faso, Zambia and Tanzania. Two models were compared in terms of their ability to predict the observed movement patterns – a gravity model, in which movement rates between pairs of locations increase with population size and decrease with distance, and a radiation model, in which travelers are cumulatively “absorbed” as they move outwards from their origin of travel. The gravity model provided a better fit to the data overall and for travel to large populations, while the radiation model provided a better fit for nearby populations. One strength of the data set was that trips could be categorized according to traveler group – namely, women traveling with children in all survey countries and youth workers in Mali. For gravity models fitted to data specific to these groups, youth workers were found to have a higher travel frequency to large population centers, and women traveling with children a lower frequency. These models may help predict the spatial transmission of malaria parasites and inform strategies to control their spread. Nature Publishing Group UK 2018-05-16 /pmc/articles/PMC5955928/ /pubmed/29769582 http://dx.doi.org/10.1038/s41598-018-26023-1 Text en © The Author(s) 2018 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/.
spellingShingle Article
Marshall, John M.
Wu, Sean L.
Sanchez C., Hector M.
Kiware, Samson S.
Ndhlovu, Micky
Ouédraogo, André Lin
Touré, Mahamoudou B.
Sturrock, Hugh J.
Ghani, Azra C.
Ferguson, Neil M.
Mathematical models of human mobility of relevance to malaria transmission in Africa
title Mathematical models of human mobility of relevance to malaria transmission in Africa
title_full Mathematical models of human mobility of relevance to malaria transmission in Africa
title_fullStr Mathematical models of human mobility of relevance to malaria transmission in Africa
title_full_unstemmed Mathematical models of human mobility of relevance to malaria transmission in Africa
title_short Mathematical models of human mobility of relevance to malaria transmission in Africa
title_sort mathematical models of human mobility of relevance to malaria transmission in africa
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5955928/
https://www.ncbi.nlm.nih.gov/pubmed/29769582
http://dx.doi.org/10.1038/s41598-018-26023-1
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