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Modeling present and future climate risk of dengue outbreak, a case study in New Caledonia
BACKGROUND: Dengue dynamics result from the complex interactions between the virus, the host and the vector, all being under the influence of the environment. Several studies explored the link between weather and dengue dynamics and some investigated the impact of climate change on these dynamics. M...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8772089/ https://www.ncbi.nlm.nih.gov/pubmed/35057822 http://dx.doi.org/10.1186/s12940-022-00829-z |
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author | Ochida, Noé Mangeas, Morgan Dupont-Rouzeyrol, Myrielle Dutheil, Cyril Forfait, Carole Peltier, Alexandre Descloux, Elodie Menkes, Christophe |
author_facet | Ochida, Noé Mangeas, Morgan Dupont-Rouzeyrol, Myrielle Dutheil, Cyril Forfait, Carole Peltier, Alexandre Descloux, Elodie Menkes, Christophe |
author_sort | Ochida, Noé |
collection | PubMed |
description | BACKGROUND: Dengue dynamics result from the complex interactions between the virus, the host and the vector, all being under the influence of the environment. Several studies explored the link between weather and dengue dynamics and some investigated the impact of climate change on these dynamics. Most attempted to predict incidence rate at a country scale or assess the environmental suitability at a global or regional scale. Here, we propose a new approach which consists in modeling the risk of dengue outbreak at a local scale according to climate conditions and study the evolution of this risk taking climate change into account. We apply this approach in New Caledonia, where high quality data are available. METHODS: We used a statistical estimation of the effective reproduction number (R(t)) based on case counts to create a categorical target variable : epidemic week/non-epidemic week. A machine learning classifier has been trained using relevant climate indicators in order to estimate the probability for a week to be epidemic under current climate data and this probability was then estimated under climate change scenarios. RESULTS: Weekly probability of dengue outbreak was best predicted with the number of days when maximal temperature exceeded 30.8°C and the mean of daily precipitation over 80 and 60 days prior to the predicted week respectively. According to scenario RCP8.5, climate will allow dengue outbreak every year in New Caledonia if the epidemiological and entomological contexts remain the same. CONCLUSION: We identified locally relevant climatic factor driving dengue outbreaks in New Caledonia and assessed the inter-annual and seasonal risk of dengue outbreak under different climate change scenarios up to the year 2100. We introduced a new modeling approach to estimate the risk of dengue outbreak depending on climate conditions. This approach is easily reproducible in other countries provided that reliable epidemiological and climate data are available. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12940-022-00829-z. |
format | Online Article Text |
id | pubmed-8772089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87720892022-01-20 Modeling present and future climate risk of dengue outbreak, a case study in New Caledonia Ochida, Noé Mangeas, Morgan Dupont-Rouzeyrol, Myrielle Dutheil, Cyril Forfait, Carole Peltier, Alexandre Descloux, Elodie Menkes, Christophe Environ Health Research BACKGROUND: Dengue dynamics result from the complex interactions between the virus, the host and the vector, all being under the influence of the environment. Several studies explored the link between weather and dengue dynamics and some investigated the impact of climate change on these dynamics. Most attempted to predict incidence rate at a country scale or assess the environmental suitability at a global or regional scale. Here, we propose a new approach which consists in modeling the risk of dengue outbreak at a local scale according to climate conditions and study the evolution of this risk taking climate change into account. We apply this approach in New Caledonia, where high quality data are available. METHODS: We used a statistical estimation of the effective reproduction number (R(t)) based on case counts to create a categorical target variable : epidemic week/non-epidemic week. A machine learning classifier has been trained using relevant climate indicators in order to estimate the probability for a week to be epidemic under current climate data and this probability was then estimated under climate change scenarios. RESULTS: Weekly probability of dengue outbreak was best predicted with the number of days when maximal temperature exceeded 30.8°C and the mean of daily precipitation over 80 and 60 days prior to the predicted week respectively. According to scenario RCP8.5, climate will allow dengue outbreak every year in New Caledonia if the epidemiological and entomological contexts remain the same. CONCLUSION: We identified locally relevant climatic factor driving dengue outbreaks in New Caledonia and assessed the inter-annual and seasonal risk of dengue outbreak under different climate change scenarios up to the year 2100. We introduced a new modeling approach to estimate the risk of dengue outbreak depending on climate conditions. This approach is easily reproducible in other countries provided that reliable epidemiological and climate data are available. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12940-022-00829-z. BioMed Central 2022-01-20 /pmc/articles/PMC8772089/ /pubmed/35057822 http://dx.doi.org/10.1186/s12940-022-00829-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Ochida, Noé Mangeas, Morgan Dupont-Rouzeyrol, Myrielle Dutheil, Cyril Forfait, Carole Peltier, Alexandre Descloux, Elodie Menkes, Christophe Modeling present and future climate risk of dengue outbreak, a case study in New Caledonia |
title | Modeling present and future climate risk of dengue outbreak, a case study in New Caledonia |
title_full | Modeling present and future climate risk of dengue outbreak, a case study in New Caledonia |
title_fullStr | Modeling present and future climate risk of dengue outbreak, a case study in New Caledonia |
title_full_unstemmed | Modeling present and future climate risk of dengue outbreak, a case study in New Caledonia |
title_short | Modeling present and future climate risk of dengue outbreak, a case study in New Caledonia |
title_sort | modeling present and future climate risk of dengue outbreak, a case study in new caledonia |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8772089/ https://www.ncbi.nlm.nih.gov/pubmed/35057822 http://dx.doi.org/10.1186/s12940-022-00829-z |
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