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Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination

BACKGROUND: Swaziland aims to eliminate malaria by 2020. However, imported cases from neighbouring endemic countries continue to sustain local parasite reservoirs and initiate transmission. As certain weather and climatic conditions may trigger or intensify malaria outbreaks, identification of areas...

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Autores principales: Chuang, Ting-Wu, Soble, Adam, Ntshalintshali, Nyasatu, Mkhonta, Nomcebo, Seyama, Eric, Mthethwa, Steven, Pindolia, Deepa, Kunene, Simon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5455096/
https://www.ncbi.nlm.nih.gov/pubmed/28571572
http://dx.doi.org/10.1186/s12936-017-1874-0
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author Chuang, Ting-Wu
Soble, Adam
Ntshalintshali, Nyasatu
Mkhonta, Nomcebo
Seyama, Eric
Mthethwa, Steven
Pindolia, Deepa
Kunene, Simon
author_facet Chuang, Ting-Wu
Soble, Adam
Ntshalintshali, Nyasatu
Mkhonta, Nomcebo
Seyama, Eric
Mthethwa, Steven
Pindolia, Deepa
Kunene, Simon
author_sort Chuang, Ting-Wu
collection PubMed
description BACKGROUND: Swaziland aims to eliminate malaria by 2020. However, imported cases from neighbouring endemic countries continue to sustain local parasite reservoirs and initiate transmission. As certain weather and climatic conditions may trigger or intensify malaria outbreaks, identification of areas prone to these conditions may aid decision-makers in deploying targeted malaria interventions more effectively. METHODS: Malaria case-surveillance data for Swaziland were provided by Swaziland’s National Malaria Control Programme. Climate data were derived from local weather stations and remote sensing images. Climate parameters and malaria cases between 2001 and 2015 were then analysed using seasonal autoregressive integrated moving average models and distributed lag non-linear models (DLNM). RESULTS: The incidence of malaria in Swaziland increased between 2005 and 2010, especially in the Lubombo and Hhohho regions. A time-series analysis indicated that warmer temperatures and higher precipitation in the Lubombo and Hhohho administrative regions are conducive to malaria transmission. DLNM showed that the risk of malaria increased in Lubombo when the maximum temperature was above 30 °C or monthly precipitation was above 5 in. In Hhohho, the minimum temperature remaining above 15 °C or precipitation being greater than 10 in. might be associated with malaria transmission. CONCLUSIONS: This study provides a preliminary assessment of the impact of short-term climate variations on malaria transmission in Swaziland. The geographic separation of imported and locally acquired malaria, as well as population behaviour, highlight the varying modes of transmission, part of which may be relevant to climate conditions. Thus, the impact of changing climate conditions should be noted as Swaziland moves toward malaria elimination. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12936-017-1874-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-54550962017-06-06 Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination Chuang, Ting-Wu Soble, Adam Ntshalintshali, Nyasatu Mkhonta, Nomcebo Seyama, Eric Mthethwa, Steven Pindolia, Deepa Kunene, Simon Malar J Research BACKGROUND: Swaziland aims to eliminate malaria by 2020. However, imported cases from neighbouring endemic countries continue to sustain local parasite reservoirs and initiate transmission. As certain weather and climatic conditions may trigger or intensify malaria outbreaks, identification of areas prone to these conditions may aid decision-makers in deploying targeted malaria interventions more effectively. METHODS: Malaria case-surveillance data for Swaziland were provided by Swaziland’s National Malaria Control Programme. Climate data were derived from local weather stations and remote sensing images. Climate parameters and malaria cases between 2001 and 2015 were then analysed using seasonal autoregressive integrated moving average models and distributed lag non-linear models (DLNM). RESULTS: The incidence of malaria in Swaziland increased between 2005 and 2010, especially in the Lubombo and Hhohho regions. A time-series analysis indicated that warmer temperatures and higher precipitation in the Lubombo and Hhohho administrative regions are conducive to malaria transmission. DLNM showed that the risk of malaria increased in Lubombo when the maximum temperature was above 30 °C or monthly precipitation was above 5 in. In Hhohho, the minimum temperature remaining above 15 °C or precipitation being greater than 10 in. might be associated with malaria transmission. CONCLUSIONS: This study provides a preliminary assessment of the impact of short-term climate variations on malaria transmission in Swaziland. The geographic separation of imported and locally acquired malaria, as well as population behaviour, highlight the varying modes of transmission, part of which may be relevant to climate conditions. Thus, the impact of changing climate conditions should be noted as Swaziland moves toward malaria elimination. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12936-017-1874-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-01 /pmc/articles/PMC5455096/ /pubmed/28571572 http://dx.doi.org/10.1186/s12936-017-1874-0 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 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.
spellingShingle Research
Chuang, Ting-Wu
Soble, Adam
Ntshalintshali, Nyasatu
Mkhonta, Nomcebo
Seyama, Eric
Mthethwa, Steven
Pindolia, Deepa
Kunene, Simon
Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination
title Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination
title_full Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination
title_fullStr Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination
title_full_unstemmed Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination
title_short Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination
title_sort assessment of climate-driven variations in malaria incidence in swaziland: toward malaria elimination
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5455096/
https://www.ncbi.nlm.nih.gov/pubmed/28571572
http://dx.doi.org/10.1186/s12936-017-1874-0
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