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Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting

In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individu...

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Autores principales: Routledge, Isobel, Chevéz, José Eduardo Romero, Cucunubá, Zulma M., Rodriguez, Manuel Gomez, Guinovart, Caterina, Gustafson, Kyle B., Schneider, Kammerle, Walker, Patrick G.T., Ghani, Azra C., Bhatt, Samir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6018772/
https://www.ncbi.nlm.nih.gov/pubmed/29946060
http://dx.doi.org/10.1038/s41467-018-04577-y
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author Routledge, Isobel
Chevéz, José Eduardo Romero
Cucunubá, Zulma M.
Rodriguez, Manuel Gomez
Guinovart, Caterina
Gustafson, Kyle B.
Schneider, Kammerle
Walker, Patrick G.T.
Ghani, Azra C.
Bhatt, Samir
author_facet Routledge, Isobel
Chevéz, José Eduardo Romero
Cucunubá, Zulma M.
Rodriguez, Manuel Gomez
Guinovart, Caterina
Gustafson, Kyle B.
Schneider, Kammerle
Walker, Patrick G.T.
Ghani, Azra C.
Bhatt, Samir
author_sort Routledge, Isobel
collection PubMed
description In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers, R(c), describe the state of transmission at a point in time and differ from mean reproduction numbers, which are averages of the number of people infected by a typical case. We assess elimination progress in El Salvador using data for confirmed cases of malaria from 2010 to 2016. Our results demonstrate that whilst the average number of secondary malaria cases was below one (0.61, 95% CI 0.55–0.65), individual reproduction numbers often exceeded one. We estimate a decline in R(c) between 2010 and 2016. However we also show that if importation is maintained at the same rate, the country may not achieve malaria elimination by 2020.
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spelling pubmed-60187722018-06-27 Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting Routledge, Isobel Chevéz, José Eduardo Romero Cucunubá, Zulma M. Rodriguez, Manuel Gomez Guinovart, Caterina Gustafson, Kyle B. Schneider, Kammerle Walker, Patrick G.T. Ghani, Azra C. Bhatt, Samir Nat Commun Article In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers, R(c), describe the state of transmission at a point in time and differ from mean reproduction numbers, which are averages of the number of people infected by a typical case. We assess elimination progress in El Salvador using data for confirmed cases of malaria from 2010 to 2016. Our results demonstrate that whilst the average number of secondary malaria cases was below one (0.61, 95% CI 0.55–0.65), individual reproduction numbers often exceeded one. We estimate a decline in R(c) between 2010 and 2016. However we also show that if importation is maintained at the same rate, the country may not achieve malaria elimination by 2020. Nature Publishing Group UK 2018-06-26 /pmc/articles/PMC6018772/ /pubmed/29946060 http://dx.doi.org/10.1038/s41467-018-04577-y Text en © The Author(s) 2018 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Routledge, Isobel
Chevéz, José Eduardo Romero
Cucunubá, Zulma M.
Rodriguez, Manuel Gomez
Guinovart, Caterina
Gustafson, Kyle B.
Schneider, Kammerle
Walker, Patrick G.T.
Ghani, Azra C.
Bhatt, Samir
Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting
title Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting
title_full Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting
title_fullStr Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting
title_full_unstemmed Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting
title_short Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting
title_sort estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6018772/
https://www.ncbi.nlm.nih.gov/pubmed/29946060
http://dx.doi.org/10.1038/s41467-018-04577-y
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