Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador
Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador
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DOI：
10.1016/s2542-5196(17)30064-5
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发表时间：
2017-07-01
影响因子：
25.7
通讯作者：
Rodo, Xavier
中科院分区：
文献类型：
--
作者：
Lowe, Rachel;Stewart-Ibarra, Anna M.;Rodo, Xavier
背景厄尔尼诺现象及其对当地气象条件的影响可能影响厄瓜多尔南部沿海登革热传播的年际变化。埃尔奥罗省是一个关键的登革热监测点，因为登革热负担很高，季节性传播，所有四种登革热血清型的共同循环，以及最近引入的基孔肯雅和寨卡病毒。在这项研究中，我们使用气候预报来预测2016年马查拉市登革热季节的演变，此前发生了有记录以来最强的厄尔尼诺事件之一。方法我们将降雨量、最低气温和Nino3.4指数预报结合到贝叶斯分层混合模型中来预测登革热发病率。该模型于2016年1月1日启动，每月发布一次登革热预报，直至2016年11月。我们通过使用主动监测数据更正被动监测记录中报告的登革热病例数据，解释了2015年由于基孔肯雅热疫情的引入而误报登革热的原因。然后，我们利用现有的流行病学信息对这一预测进行了回顾评估。预测结果正确地预测了2016年3月登革热发病的早期高峰，有90%的可能性超过了过去5年的平均登革热发病率。考虑到2015年被错误记录为登革热的基孔肯雅病例的比例，改进了对2016年登革热发病率的预测。解释这一登革热预测框架，使用季节性气候和厄尔尼诺预测，允许在年初对整个登革热季节进行预测。将活跃的监测数据与常规的登革热报告相结合，不仅改善了模型的拟合和性能，而且提高了基于历史季节性平均的基准估计的准确性。这项研究通过展示将气候信息纳入厄瓜多尔公共卫生决策过程的潜在价值，推动了为卫生部门提供气候服务的最先进水平。版权所有(C)作者(S)。爱思唯尔有限公司出版。
Background El Nino and its effect on local meteorological conditions potentially influences interannual variability in dengue transmission in southern coastal Ecuador. El Oro province is a key dengue surveillance site, due to the high burden of dengue, seasonal transmission, co-circulation of all four dengue serotypes, and the recent introduction of chikungunya and Zika. In this study, we used climate forecasts to predict the evolution of the 2016 dengue season in the city of Machala, following one of the strongest El Nino events on record.Methods We incorporated precipitation, minimum temperature, and Nino3.4 index forecasts in a Bayesian hierarchical mixed model to predict dengue incidence. The model was initiated on Jan 1, 2016, producing monthly dengue forecasts until November, 2016. We accounted for misreporting of dengue due to the introduction of chikungunya in 2015, by using active surveillance data to correct reported dengue case data from passive surveillance records. We then evaluated the forecast retrospectively with available epidemiological information.Findings The predictions correctly forecast an early peak in dengue incidence in March, 2016, with a 90% chance of exceeding the mean dengue incidence for the previous 5 years. Accounting for the proportion of chikungunya cases that had been incorrectly recorded as dengue in 2015 improved the prediction of the magnitude of dengue incidence in 2016.Interpretation This dengue prediction framework, which uses seasonal climate and El Nino forecasts, allows a prediction to be made at the start of the year for the entire dengue season. Combining active surveillance data with routine dengue reports improved not only model fit and performance, but also the accuracy of benchmark estimates based on historical seasonal averages. This study advances the state-of-the-art of climate services for the health sector, by showing the potential value of incorporating climate information in the public health decision-making process in Ecuador. Copyright (C) The Author(s). Published by Elsevier Ltd.