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Potential Predictability of Malaria in Africa Using ECMWF Monthly and Seasonal Climate Forecasts

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  • 1 Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
  • | 2 European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
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Abstract

Idealized model experiments investigate the advance warning for malaria that may be presently possible using temperature and rainfall predictions from state-of-the-art operational monthly and seasonal weather-prediction systems. The climate forecasts drive a dynamical malaria model for all of Africa, and the predictions are evaluated using reanalysis data. The regions and months for which climate is responsible for significant interannual malaria transmission variability are first identified. In addition to epidemic-prone zones these also include hyperendemic regions subject to high variability during specific months of the year, often associated with the monsoon onset. In many of these areas, temperature anomalies are predictable from 1 to 2 months ahead, and reliable precipitation forecasts are available in eastern and southern Africa 1 month ahead. The inherent lag between the rainy seasons and malaria transmission results in potential predictability in malaria transmission 3–4 months in advance, extending the early warning available from environmental monitoring by 1–2 months, although the realizable forecast skill will be less than this because of an imperfect malaria model. A preliminary examination of the forecasts for the highlands of Uganda and Kenya shows that the system is able to predict the years during the last two decades in which documented highland outbreaks occurred, in particular the major event of 1998, but that the timing of outbreaks was often imprecise and inconsistent across lead times. In addition to country-level evaluation with district health data, issues that need addressing to integrate such a climate-based prediction system into health-decision processes are briefly discussed.

Corresponding author address: Adrian Tompkins, ICTP, Earth System Physics, Strada Costiera 11, Trieste, TS 34151, Italy. E-mail: tompkins@ictp.it

Abstract

Idealized model experiments investigate the advance warning for malaria that may be presently possible using temperature and rainfall predictions from state-of-the-art operational monthly and seasonal weather-prediction systems. The climate forecasts drive a dynamical malaria model for all of Africa, and the predictions are evaluated using reanalysis data. The regions and months for which climate is responsible for significant interannual malaria transmission variability are first identified. In addition to epidemic-prone zones these also include hyperendemic regions subject to high variability during specific months of the year, often associated with the monsoon onset. In many of these areas, temperature anomalies are predictable from 1 to 2 months ahead, and reliable precipitation forecasts are available in eastern and southern Africa 1 month ahead. The inherent lag between the rainy seasons and malaria transmission results in potential predictability in malaria transmission 3–4 months in advance, extending the early warning available from environmental monitoring by 1–2 months, although the realizable forecast skill will be less than this because of an imperfect malaria model. A preliminary examination of the forecasts for the highlands of Uganda and Kenya shows that the system is able to predict the years during the last two decades in which documented highland outbreaks occurred, in particular the major event of 1998, but that the timing of outbreaks was often imprecise and inconsistent across lead times. In addition to country-level evaluation with district health data, issues that need addressing to integrate such a climate-based prediction system into health-decision processes are briefly discussed.

Corresponding author address: Adrian Tompkins, ICTP, Earth System Physics, Strada Costiera 11, Trieste, TS 34151, Italy. E-mail: tompkins@ictp.it
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