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Characterising malaria connectivity using malaria indicator survey data

Malaria connectivity describes the flow of parasites among transmission sources and sinks within a given landscape. Because of the spatial and temporal scales at which parasites are transported by their hosts, malaria sub-populations are largely defined by mosquito movement and malaria connectivity...

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Autores principales: Guerra, Carlos A., Citron, Daniel T., García, Guillermo A., Smith, David L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929427/
https://www.ncbi.nlm.nih.gov/pubmed/31870353
http://dx.doi.org/10.1186/s12936-019-3078-2
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author Guerra, Carlos A.
Citron, Daniel T.
García, Guillermo A.
Smith, David L.
author_facet Guerra, Carlos A.
Citron, Daniel T.
García, Guillermo A.
Smith, David L.
author_sort Guerra, Carlos A.
collection PubMed
description Malaria connectivity describes the flow of parasites among transmission sources and sinks within a given landscape. Because of the spatial and temporal scales at which parasites are transported by their hosts, malaria sub-populations are largely defined by mosquito movement and malaria connectivity among them is largely driven by human movement. Characterising malaria connectivity thus requires characterising human travel between areas with differing levels of exposure to malaria. Whilst understanding malaria connectivity is fundamental for optimising interventions, particularly in areas seeking or sustaining elimination, there is a dearth of human movement data required to achieve this goal. Malaria indicator surveys (MIS) are a generally under utilised but potentially rich source of travel data that provide a unique opportunity to study simple associations between malaria infection and human travel in large population samples. This paper shares the experience working with MIS data from Bioko Island that revealed programmatically useful information regarding malaria importation through human travel. Simple additions to MIS questionnaires greatly augmented the level of detail of the travel data, which can be used to characterise human travel patterns and malaria connectivity to assist targeting interventions. It is argued that MIS potentially represent very important and timely sources of travel data that need to be further exploited.
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spelling pubmed-69294272019-12-30 Characterising malaria connectivity using malaria indicator survey data Guerra, Carlos A. Citron, Daniel T. García, Guillermo A. Smith, David L. Malar J Commentary Malaria connectivity describes the flow of parasites among transmission sources and sinks within a given landscape. Because of the spatial and temporal scales at which parasites are transported by their hosts, malaria sub-populations are largely defined by mosquito movement and malaria connectivity among them is largely driven by human movement. Characterising malaria connectivity thus requires characterising human travel between areas with differing levels of exposure to malaria. Whilst understanding malaria connectivity is fundamental for optimising interventions, particularly in areas seeking or sustaining elimination, there is a dearth of human movement data required to achieve this goal. Malaria indicator surveys (MIS) are a generally under utilised but potentially rich source of travel data that provide a unique opportunity to study simple associations between malaria infection and human travel in large population samples. This paper shares the experience working with MIS data from Bioko Island that revealed programmatically useful information regarding malaria importation through human travel. Simple additions to MIS questionnaires greatly augmented the level of detail of the travel data, which can be used to characterise human travel patterns and malaria connectivity to assist targeting interventions. It is argued that MIS potentially represent very important and timely sources of travel data that need to be further exploited. BioMed Central 2019-12-23 /pmc/articles/PMC6929427/ /pubmed/31870353 http://dx.doi.org/10.1186/s12936-019-3078-2 Text en © The Author(s) 2019 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Commentary
Guerra, Carlos A.
Citron, Daniel T.
García, Guillermo A.
Smith, David L.
Characterising malaria connectivity using malaria indicator survey data
title Characterising malaria connectivity using malaria indicator survey data
title_full Characterising malaria connectivity using malaria indicator survey data
title_fullStr Characterising malaria connectivity using malaria indicator survey data
title_full_unstemmed Characterising malaria connectivity using malaria indicator survey data
title_short Characterising malaria connectivity using malaria indicator survey data
title_sort characterising malaria connectivity using malaria indicator survey data
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929427/
https://www.ncbi.nlm.nih.gov/pubmed/31870353
http://dx.doi.org/10.1186/s12936-019-3078-2
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