Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators
Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators
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DOI：
10.1371/journal.pntd.0004681
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发表时间：
2016-04-01
影响因子：
3.8
通讯作者：
Flamand, Claude
中科院分区：
文献类型：
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作者：
Adde, Antoine;Roucou, Pascal;Flamand, Claude
背景登革热流行动力学是由宿主、媒介和病毒之间复杂的相互作用驱动的。世界各地都在研究气候与登革热之间的关系，但结果表明，气候的影响在不同的研究地点之间可能存在很大差异。在法属圭亚那，没有基于气候的模型来协助建立预警系统。本研究旨在评估使用海洋和大气条件，以帮助预测登革热疫情在法属Guiana.Methodology/Principal FindingsLagged相关性和复合分析的潜力，以确定气候条件，其特点是一个典型的流行年，并确定在1991-2013年期间预测登革热疫情的最佳指标。然后进行逻辑回归以建立预测模型。我们证明，基于夏季赤道太平洋海面温度和亚速尔群岛高海平面压力的模型具有预测价值，能够预测80%的疫情，而错误地预测只有15%的非流行年份。2014-2015年的预测与观察到的非流行性条件一致，并预测在2016年初爆发。结论/显著性这些研究结果表明，可以使用一个简单的气候指标组合来模拟疫情死灰复燃。这可能有助于预测公共卫生行动，以减轻重大疫情的影响，特别是在资源有限和医疗基础设施普遍不足的地区。
BackgroundDengue 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 FindingsLagged 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/SignificanceThese 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.