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Using a Geographical-Information-System-Based Decision Support to Enhance Malaria Vector Control in Zambia
Geographic information systems (GISs) with emerging technologies are being harnessed for studying spatial patterns in vector-borne diseases to reduce transmission. To implement effective vector control, increased knowledge on interactions of epidemiological and entomological malaria transmission det...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3323906/ https://www.ncbi.nlm.nih.gov/pubmed/22548086 http://dx.doi.org/10.1155/2012/363520 |
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author | Chanda, Emmanuel Mukonka, Victor Munyongwe Mthembu, David Kamuliwo, Mulakwa Coetzer, Sarel Shinondo, Cecilia Jill |
author_facet | Chanda, Emmanuel Mukonka, Victor Munyongwe Mthembu, David Kamuliwo, Mulakwa Coetzer, Sarel Shinondo, Cecilia Jill |
author_sort | Chanda, Emmanuel |
collection | PubMed |
description | Geographic information systems (GISs) with emerging technologies are being harnessed for studying spatial patterns in vector-borne diseases to reduce transmission. To implement effective vector control, increased knowledge on interactions of epidemiological and entomological malaria transmission determinants in the assessment of impact of interventions is critical. This requires availability of relevant spatial and attribute data to support malaria surveillance, monitoring, and evaluation. Monitoring the impact of vector control through a GIS-based decision support (DSS) has revealed spatial relative change in prevalence of infection and vector susceptibility to insecticides and has enabled measurement of spatial heterogeneity of trend or impact. The revealed trends and interrelationships have allowed the identification of areas with reduced parasitaemia and increased insecticide resistance thus demonstrating the impact of resistance on vector control. The GIS-based DSS provides opportunity for rational policy formulation and cost-effective utilization of limited resources for enhanced malaria vector control. |
format | Online Article Text |
id | pubmed-3323906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-33239062012-04-30 Using a Geographical-Information-System-Based Decision Support to Enhance Malaria Vector Control in Zambia Chanda, Emmanuel Mukonka, Victor Munyongwe Mthembu, David Kamuliwo, Mulakwa Coetzer, Sarel Shinondo, Cecilia Jill J Trop Med Review Article Geographic information systems (GISs) with emerging technologies are being harnessed for studying spatial patterns in vector-borne diseases to reduce transmission. To implement effective vector control, increased knowledge on interactions of epidemiological and entomological malaria transmission determinants in the assessment of impact of interventions is critical. This requires availability of relevant spatial and attribute data to support malaria surveillance, monitoring, and evaluation. Monitoring the impact of vector control through a GIS-based decision support (DSS) has revealed spatial relative change in prevalence of infection and vector susceptibility to insecticides and has enabled measurement of spatial heterogeneity of trend or impact. The revealed trends and interrelationships have allowed the identification of areas with reduced parasitaemia and increased insecticide resistance thus demonstrating the impact of resistance on vector control. The GIS-based DSS provides opportunity for rational policy formulation and cost-effective utilization of limited resources for enhanced malaria vector control. Hindawi Publishing Corporation 2012 2012-04-02 /pmc/articles/PMC3323906/ /pubmed/22548086 http://dx.doi.org/10.1155/2012/363520 Text en Copyright © 2012 Emmanuel Chanda et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Chanda, Emmanuel Mukonka, Victor Munyongwe Mthembu, David Kamuliwo, Mulakwa Coetzer, Sarel Shinondo, Cecilia Jill Using a Geographical-Information-System-Based Decision Support to Enhance Malaria Vector Control in Zambia |
title | Using a Geographical-Information-System-Based Decision Support to Enhance Malaria Vector Control in Zambia |
title_full | Using a Geographical-Information-System-Based Decision Support to Enhance Malaria Vector Control in Zambia |
title_fullStr | Using a Geographical-Information-System-Based Decision Support to Enhance Malaria Vector Control in Zambia |
title_full_unstemmed | Using a Geographical-Information-System-Based Decision Support to Enhance Malaria Vector Control in Zambia |
title_short | Using a Geographical-Information-System-Based Decision Support to Enhance Malaria Vector Control in Zambia |
title_sort | using a geographical-information-system-based decision support to enhance malaria vector control in zambia |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3323906/ https://www.ncbi.nlm.nih.gov/pubmed/22548086 http://dx.doi.org/10.1155/2012/363520 |
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