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• W2021470958 abstract "Botswana has compiled a continuous record of the incidence of malaria for the period 1982–2002, providing a unique data set for work on malaria epidemiology in a desert-fringe area. Climate fluctuations are known to be a major determinant of malaria transmission in parts of Africa where the disease is endemic. Based on established quantitative relationships between climate fluctuations and malaria incidence, a new system for predicting interannual climate fluctuations in epidemic-prone regions has now been developed. The DEMETER project, combining the leading European global climate prediction models, can successfully predict the probability of a malaria outbreak in Botswana up to five months ahead, providing an extra four months warning compared with current monitoring methods, during which time vital decisions about resource allocation can be made. The control of epidemic malaria is a priority for the international health community and specific targets for the early detection and effective control of epidemics have been agreed1. Interannual climate variability is an important determinant of epidemics in parts of Africa2 where climate drives both mosquito vector dynamics and parasite development rates3. Hence, skilful seasonal climate forecasts may provide early warning of changes of risk in epidemic-prone regions. Here we discuss the development of a system to forecast probabilities of anomalously high and low malaria incidence with dynamically based, seasonal-timescale, multi-model ensemble predictions of climate, using leading global coupled ocean–atmosphere climate models developed in Europe. This forecast system is successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability are well established4, adding up to four months lead time over malaria warnings issued with observed precipitation and having a comparably high level of probabilistic prediction skill. In years in which the forecast probability distribution is different from that of climatology, malaria decision-makers can use this information for improved resource allocation." @default.
• W2021470958 created "2016-06-24" @default.
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• W2021470958 date "2006-02-01" @default.
• W2021470958 modified "2023-10-16" @default.
• W2021470958 title "Malaria early warnings based on seasonal climate forecasts from multi-model ensembles" @default.
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• W2021470958 doi "https://doi.org/10.1038/nature04503" @default.
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