Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators.

BACKGROUND:Dengue fever epidemic dynamics are driven by complex interactions between hosts, vectors and viruses. Associations between climate and dengue have been studied around the world, but the results have shown that the impact of the climate can vary widely from one study site to another. In Fr...

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Bibliographic Details
Published in:PLOS Neglected Tropical Diseases
Main Authors: Antoine Adde, Pascal Roucou, Morgan Mangeas, Vanessa Ardillon, Jean-Claude Desenclos, Dominique Rousset, Romain Girod, Sébastien Briolant, Philippe Quenel, Claude Flamand
Format: Article in Journal/Newspaper
Language:English
Published: Public Library of Science (PLoS) 2016
Subjects:
Arctic medicine. Tropical medicine
RC955-962
Public aspects of medicine
RA1-1270
Arctic
Pacific
Online Access:https://doi.org/10.1371/journal.pntd.0004681
https://doaj.org/article/e4ff24ed5f904e3ea84b032139558b5e
Description
Summary:BACKGROUND:Dengue fever epidemic dynamics are driven by complex interactions between hosts, vectors and viruses. Associations between climate and dengue have been studied around the world, but the results have shown that the impact of the climate can vary widely from one study site to another. In French Guiana, climate-based models are not available to assist in developing an early warning system. This study aims to evaluate the potential of using oceanic and atmospheric conditions to help predict dengue fever outbreaks in French Guiana. METHODOLOGY/PRINCIPAL FINDINGS:Lagged correlations and composite analyses were performed to identify the climatic conditions that characterized a typical epidemic year and to define the best indices for predicting dengue fever outbreaks during the period 1991-2013. A logistic regression was then performed to build a forecast model. We demonstrate that a model based on summer Equatorial Pacific Ocean sea surface temperatures and Azores High sea-level pressure had predictive value and was able to predict 80% of the outbreaks while incorrectly predicting only 15% of the non-epidemic years. Predictions for 2014-2015 were consistent with the observed non-epidemic conditions, and an outbreak in early 2016 was predicted. CONCLUSIONS/SIGNIFICANCE:These findings indicate that outbreak resurgence can be modeled using a simple combination of climate indicators. This might be useful for anticipating public health actions to mitigate the effects of major outbreaks, particularly in areas where resources are limited and medical infrastructures are generally insufficient.

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