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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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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. |
format | Online Article Text |
id | pubmed-6018772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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