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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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 |
work_keys_str_mv |
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