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Climate-Based Models for Understanding and Forecasting Dengue Epidemics

BACKGROUND: Dengue dynamics are driven by complex interactions between human-hosts, mosquito-vectors and viruses that are influenced by environmental and climatic factors. The objectives of this study were to analyze and model the relationships between climate, Aedes aegypti vectors and dengue outbr...

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Autores principales: Descloux, Elodie, Mangeas, Morgan, Menkes, Christophe Eugène, Lengaigne, Matthieu, Leroy, Anne, Tehei, Temaui, Guillaumot, Laurent, Teurlai, Magali, Gourinat, Ann-Claire, Benzler, Justus, Pfannstiel, Anne, Grangeon, Jean-Paul, Degallier, Nicolas, De Lamballerie, Xavier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3279338/
https://www.ncbi.nlm.nih.gov/pubmed/22348154
http://dx.doi.org/10.1371/journal.pntd.0001470
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author Descloux, Elodie
Mangeas, Morgan
Menkes, Christophe Eugène
Lengaigne, Matthieu
Leroy, Anne
Tehei, Temaui
Guillaumot, Laurent
Teurlai, Magali
Gourinat, Ann-Claire
Benzler, Justus
Pfannstiel, Anne
Grangeon, Jean-Paul
Degallier, Nicolas
De Lamballerie, Xavier
author_facet Descloux, Elodie
Mangeas, Morgan
Menkes, Christophe Eugène
Lengaigne, Matthieu
Leroy, Anne
Tehei, Temaui
Guillaumot, Laurent
Teurlai, Magali
Gourinat, Ann-Claire
Benzler, Justus
Pfannstiel, Anne
Grangeon, Jean-Paul
Degallier, Nicolas
De Lamballerie, Xavier
author_sort Descloux, Elodie
collection PubMed
description BACKGROUND: Dengue dynamics are driven by complex interactions between human-hosts, mosquito-vectors and viruses that are influenced by environmental and climatic factors. The objectives of this study were to analyze and model the relationships between climate, Aedes aegypti vectors and dengue outbreaks in Noumea (New Caledonia), and to provide an early warning system. METHODOLOGY/PRINCIPAL FINDINGS: Epidemiological and meteorological data were analyzed from 1971 to 2010 in Noumea. Entomological surveillance indices were available from March 2000 to December 2009. During epidemic years, the distribution of dengue cases was highly seasonal. The epidemic peak (March–April) lagged the warmest temperature by 1–2 months and was in phase with maximum precipitations, relative humidity and entomological indices. Significant inter-annual correlations were observed between the risk of outbreak and summertime temperature, precipitations or relative humidity but not ENSO. Climate-based multivariate non-linear models were developed to estimate the yearly risk of dengue outbreak in Noumea. The best explicative meteorological variables were the number of days with maximal temperature exceeding 32°C during January–February–March and the number of days with maximal relative humidity exceeding 95% during January. The best predictive variables were the maximal temperature in December and maximal relative humidity during October–November–December of the previous year. For a probability of dengue outbreak above 65% in leave-one-out cross validation, the explicative model predicted 94% of the epidemic years and 79% of the non epidemic years, and the predictive model 79% and 65%, respectively. CONCLUSIONS/SIGNIFICANCE: The epidemic dynamics of dengue in Noumea were essentially driven by climate during the last forty years. Specific conditions based on maximal temperature and relative humidity thresholds were determinant in outbreaks occurrence. Their persistence was also crucial. An operational model that will enable health authorities to anticipate the outbreak risk was successfully developed. Similar models may be developed to improve dengue management in other countries.
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spelling pubmed-32793382012-02-17 Climate-Based Models for Understanding and Forecasting Dengue Epidemics Descloux, Elodie Mangeas, Morgan Menkes, Christophe Eugène Lengaigne, Matthieu Leroy, Anne Tehei, Temaui Guillaumot, Laurent Teurlai, Magali Gourinat, Ann-Claire Benzler, Justus Pfannstiel, Anne Grangeon, Jean-Paul Degallier, Nicolas De Lamballerie, Xavier PLoS Negl Trop Dis Research Article BACKGROUND: Dengue dynamics are driven by complex interactions between human-hosts, mosquito-vectors and viruses that are influenced by environmental and climatic factors. The objectives of this study were to analyze and model the relationships between climate, Aedes aegypti vectors and dengue outbreaks in Noumea (New Caledonia), and to provide an early warning system. METHODOLOGY/PRINCIPAL FINDINGS: Epidemiological and meteorological data were analyzed from 1971 to 2010 in Noumea. Entomological surveillance indices were available from March 2000 to December 2009. During epidemic years, the distribution of dengue cases was highly seasonal. The epidemic peak (March–April) lagged the warmest temperature by 1–2 months and was in phase with maximum precipitations, relative humidity and entomological indices. Significant inter-annual correlations were observed between the risk of outbreak and summertime temperature, precipitations or relative humidity but not ENSO. Climate-based multivariate non-linear models were developed to estimate the yearly risk of dengue outbreak in Noumea. The best explicative meteorological variables were the number of days with maximal temperature exceeding 32°C during January–February–March and the number of days with maximal relative humidity exceeding 95% during January. The best predictive variables were the maximal temperature in December and maximal relative humidity during October–November–December of the previous year. For a probability of dengue outbreak above 65% in leave-one-out cross validation, the explicative model predicted 94% of the epidemic years and 79% of the non epidemic years, and the predictive model 79% and 65%, respectively. CONCLUSIONS/SIGNIFICANCE: The epidemic dynamics of dengue in Noumea were essentially driven by climate during the last forty years. Specific conditions based on maximal temperature and relative humidity thresholds were determinant in outbreaks occurrence. Their persistence was also crucial. An operational model that will enable health authorities to anticipate the outbreak risk was successfully developed. Similar models may be developed to improve dengue management in other countries. Public Library of Science 2012-02-14 /pmc/articles/PMC3279338/ /pubmed/22348154 http://dx.doi.org/10.1371/journal.pntd.0001470 Text en Descloux et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Descloux, Elodie
Mangeas, Morgan
Menkes, Christophe Eugène
Lengaigne, Matthieu
Leroy, Anne
Tehei, Temaui
Guillaumot, Laurent
Teurlai, Magali
Gourinat, Ann-Claire
Benzler, Justus
Pfannstiel, Anne
Grangeon, Jean-Paul
Degallier, Nicolas
De Lamballerie, Xavier
Climate-Based Models for Understanding and Forecasting Dengue Epidemics
title Climate-Based Models for Understanding and Forecasting Dengue Epidemics
title_full Climate-Based Models for Understanding and Forecasting Dengue Epidemics
title_fullStr Climate-Based Models for Understanding and Forecasting Dengue Epidemics
title_full_unstemmed Climate-Based Models for Understanding and Forecasting Dengue Epidemics
title_short Climate-Based Models for Understanding and Forecasting Dengue Epidemics
title_sort climate-based models for understanding and forecasting dengue epidemics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3279338/
https://www.ncbi.nlm.nih.gov/pubmed/22348154
http://dx.doi.org/10.1371/journal.pntd.0001470
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