Centre for the Mathematical Modelling of Infectious Diseases and Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; CLIMA-Climate and Health Programme, Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain.
Center for Global Health and Translational Science and Department of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Lancet Planet Health. 2017 Jul;1(4):e142-e151. doi: 10.1016/S2542-5196(17)30064-5. Epub 2017 Jul 7.
El Niño 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 Niño events on record.
We incorporated precipitation, minimum temperature, and Niño3·4 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.
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.
This dengue prediction framework, which uses seasonal climate and El Niño 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.
European Union FP7, Royal Society, and National Science Foundation.
厄尔尼诺及其对当地气象条件的影响可能会影响厄瓜多尔南部沿海地区登革热传播的年际变化。埃尔奥罗省是登革热监测的重点地区，这是由于该地区登革热负担沉重、季节性传播、四种登革热血清型同时流行以及寨卡和基孔肯雅热的新近传入。在这项研究中，我们使用气候预测来预测马查拉市 2016 年登革热季节的演变，这是有记录以来最强的厄尔尼诺事件之一。
我们将降水、最低温度和 Niño3·4 指数预测纳入贝叶斯分层混合模型中，以预测登革热发病率。该模型于 2016 年 1 月 1 日启动，直至 2016 年 11 月，每月产生登革热预测。我们通过使用主动监测数据纠正 2015 年基孔肯雅热引入后被动监测记录中报告的登革热病例数据，从而解释了由于 2015 年基孔肯雅热的引入而导致的登革热报告的错误。然后，我们根据可用的流行病学信息对预测进行回顾性评估。
预测正确地预测了 2016 年 3 月登革热发病率的早期高峰，发病率超过过去 5 年平均发病率的可能性为 90%。考虑到 2015 年基孔肯雅热病例被错误记录为登革热的比例，提高了 2016 年登革热发病率的预测准确性。
这种使用季节性气候和厄尔尼诺预测的登革热预测框架，可以在年初对整个登革热季节进行预测。将主动监测数据与常规登革热报告相结合，不仅提高了模型的拟合度和性能，还提高了基于历史季节性平均值的基准估计的准确性。这项研究通过展示厄瓜多尔将气候信息纳入公共卫生决策过程的潜在价值，推进了卫生部门气候服务的最新进展。
欧盟 FP7、英国皇家学会和美国国家科学基金会。
