Characterising malaria connectivity using malaria indicator survey data

Abstract 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 conn...

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Main Authors: Carlos A. Guerra, Daniel T. Citron, Guillermo A. García, David L. Smith
Format: Article
Language:English
Published: BMC 2019-12-01
Series:Malaria Journal
Subjects:
Online Access:https://doi.org/10.1186/s12936-019-3078-2
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spelling doaj-323ada4e4a9c400b82fa6ebe841f5aa62020-12-27T12:19:56ZengBMCMalaria Journal1475-28752019-12-0118111210.1186/s12936-019-3078-2Characterising malaria connectivity using malaria indicator survey dataCarlos A. Guerra0Daniel T. Citron1Guillermo A. García2David L. Smith3Medical Care Development InternationalInstitute for Health Metrics and Evaluation, University of WashingtonMedical Care Development InternationalInstitute for Health Metrics and Evaluation, University of WashingtonAbstract 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.https://doi.org/10.1186/s12936-019-3078-2Malaria connectivityMalaria importationMalaria indicator surveyHuman mobilityHuman movementHuman travel
collection DOAJ
language English
format Article
sources DOAJ
author Carlos A. Guerra
Daniel T. Citron
Guillermo A. García
David L. Smith
spellingShingle Carlos A. Guerra
Daniel T. Citron
Guillermo A. García
David L. Smith
Characterising malaria connectivity using malaria indicator survey data
Malaria Journal
Malaria connectivity
Malaria importation
Malaria indicator survey
Human mobility
Human movement
Human travel
author_facet Carlos A. Guerra
Daniel T. Citron
Guillermo A. García
David L. Smith
author_sort Carlos A. Guerra
title Characterising malaria connectivity using malaria indicator survey data
title_short 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_sort characterising malaria connectivity using malaria indicator survey data
publisher BMC
series Malaria Journal
issn 1475-2875
publishDate 2019-12-01
description Abstract 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.
topic Malaria connectivity
Malaria importation
Malaria indicator survey
Human mobility
Human movement
Human travel
url https://doi.org/10.1186/s12936-019-3078-2
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