State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Beijing Normal University, Beijing, China.
Yangtze Eco-Environment Engineering Research Center, China Three Gorges Corporation, Wuhan, China.
Science. 2024 May 10;384(6696):639-646. doi: 10.1126/science.adj4427. Epub 2024 May 9.
Despite identifying El Niño events as a factor in dengue dynamics, predicting the oscillation of global dengue epidemics remains challenging. Here, we investigate climate indicators and worldwide dengue incidence from 1990 to 2019 using climate-driven mechanistic models. We identify a distinct indicator, the Indian Ocean basin-wide (IOBW) index, as representing the regional average of sea surface temperature anomalies in the tropical Indian Ocean. IOBW is closely associated with dengue epidemics for both the Northern and Southern hemispheres. The ability of IOBW to predict dengue incidence likely arises as a result of its effect on local temperature anomalies through teleconnections. These findings indicate that the IOBW index can potentially enhance the lead time for dengue forecasts, leading to better-planned and more impactful outbreak responses.
尽管已经确定厄尔尼诺现象是登革热动态变化的一个因素，但预测全球登革热疫情的波动仍然具有挑战性。在这里，我们使用气候驱动的机制模型研究了 1990 年至 2019 年期间的气候指标和全球登革热发病率。我们确定了一个独特的指标，即印度洋全流域（IOBW）指数，该指数代表热带印度洋中海表温度异常的区域平均值。IOBW 与北半球和南半球的登革热流行都密切相关。IOBW 能够预测登革热发病率，这可能是因为它通过遥相关对当地温度异常产生影响。这些发现表明，IOBW 指数有可能提高登革热预测的提前期，从而实现更有计划和更有影响力的疫情应对。
