News Release

Indian ocean temperature anomalies predict global dengue trends

Peer-Reviewed Publication

American Association for the Advancement of Science (AAAS)

Sea surface temperature anomalies in the Indian Ocean predict the magnitude of global dengue epidemics, according to a new study. The findings suggest that the climate indicator could enhance the forecasting and planning for outbreak responses. Dengue – a mosquito-borne flavivirus disease – affects nearly half the world’s population. Currently, there are no specific drugs or vaccines for the disease, and outbreaks can have serious public health and economic impacts. As a result, the ability to predict the risk of outbreaks and prepare accordingly is crucial for many regions where the disease is endemic. Current dengue early warning systems use climate indicators, like precipitation and temperature, to forecast disease trends. For example, El Niño climate events are known to influence the dynamics of dengue transmission globally by affecting mosquito breeding. However, the long-distance climate drivers on dengue outbreaks are poorly understood. Using climate-driven mechanistic models and data on dengue cases reported from 46 countries in Southeast Asia and America, Yuyang Chen and colleagues modeled associations between global climate patterns and the seasonal and interannual magnitude of dengue epidemics. Chen et al. discovered that the Indian Ocean basin-wide (IOBW) index – the regional average of sea surface temperature anomalies in the tropical Indian Ocean – is closely associated with dengue epidemics for both the Northern and Southern Hemispheres. According to the findings, the IOBW index in the 3 months before the dengue season is a crucial factor in predicting the disease magnitude and timing of dengue outbreaks per year in each hemisphere. The ability of IOBW to predict dengue incidence likely arises due to its effect on regional temperatures. While the authors argue that the findings could allow for more effective planning for outbreak response, further assessments are needed to evaluate the predictive performance of the model. “Although our model demonstrates its capability to capture observed patterns, making premature claims about its predictive ability without rigorous validation of future data would be unjustified,” Chen et al. write.

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