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.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
WHO/UNICEF. The World Malaria Report. 1–120, Report no. WHO/HTM/MAL/2005.1102 (WHO/UNICEF, Geneva, 2005); http://rbm.who.int/wmr2005/index.html
Najera, J. A., Kouznetzsov, R. L. & Delacollette, C. Malaria Epidemics: Detection And Control, Forecasting And Prevention. 1–81, Report no. WHO/MAL/98.1084 (WHO, Geneva, 1998); http://www.who.int/malaria/docs/najera_epidemics/naj_toc.htm
Thomson, M. C., Connor, S. J., Milligan, P. J. M. & Flasse, S. P. The ecology of malaria—as seen from Earth-observation satellites. Ann. Trop. Med. Parasitol. 90, 243–264 (1996)
Thomson, M. C., Connor, S. J., Phindela, T. & Mason, S. J. Rainfall and sea-surface temperature monitoring for malaria early warning in Botswana. Am. J. Trop. Med. Hyg. 73, 214–221 (2005)
Greenwood, B. M., Bojang, K., Whitty, C. & Targett, G. A. T. Malaria. Lancet 365, 1487–1498 (2005)
Worrall, E., Rietveld, A. & Delacollette, C. The burden of malaria epidemics and cost-effectiveness of interventions in epidemic situations in Africa. Am. J. Trop. Med. Hyg. 71 (Suppl. 2), 136–140 (2004)
WHO. Malaria Early Warning Systems, Concepts, Indicators and Partners. A framework for Field Research in Africa. 1–84, Report no. WHO/CDS/RBM/2001.32 (WHO/Roll Back Malaria/Technical Support Network for Prevention and Control of Malaria, Geneva, 2001); http://mosquito.who.int/cmc_upload/0/000/014/807/mews2.pdf
Palmer, T. N. et al. Development of a European ensemble system for seasonal to inter-annual prediction. Bull. Am. Meteorol. Soc. 85, 853–872 (2004)
Palmer, T. N. et al. Representing model uncertainty in weather and climate prediction. Annu. Rev. Earth Planet. Sci. 33, 163–193 (2005)
Goddard, L. et al. Current approaches to seasonal-to-interannual climate predictions. Int. J. Climatol. 21, 1111–1152 (2001)
Stockdale, T. N., Anderson, D. L. T., Alves, J. O. S. & Balmaseda, M. A. Global seasonal rainfall forecasts using a coupled ocean-atmosphere model. Nature 392, 370–373 (1998)
Cane, M., Eshel, G. & Buckland, R. Forecasting Zimbabwean maize yield using eastern equatorial Pacific sea surface temperature. Nature 370, 204–205 (1994)
Coelho, C. A. S., Pezzulli, S., Balmaseda, M., Doblas-Reyes, F. J. & Stephenson, D. B. Forecast calibration and combination: A simple Bayesian approach for ENSO. J. Clim. 17, 1504–1516 (2004)
Jolliffe, I. T. & Stephenson, D. B. in A Practitioner's Guide in Atmospheric Science (eds Jolliffe, I. T. & Stephenson, D. B.) 1–12 (I.T. Wiley, Chichester, 2003)
Richardson, D. S. Skill and relative economic value of the ECMWF ensemble prediction system. Q. J.R. Meteorol. Soc. 126, 649–667 (2000)
DaSilva, J. et al. Improving epidemic malaria planning, preparedness and response in southern Africa. Malaria J. 3, doi:10.1186/1475-2875-3-37 (2004)
Brinkmann, U. & Brinkmann, A. Malaria and health in Africa—the present situation and epidemiologic trends. Trop. Med. Parasitol. 42, 204–213 (1991)
Mason, S. J. & Tyson, P. D. in Droughts Vol. 1 A Global Assessment (ed. Wilhite, D. A.) 113–134 (Routledge, New York, 2000)
Xie, P. & Arkin, P. A. Global monthly precipitation from satellite-observed outgoing longwave radiation. J. Clim. 11, 137–164 (1998)
Hagedorn, R., Doblas-Reyes, F. J. & Palmer, T. N. The rationale behind the success of multi-model ensembles in seasonal forecasting. Part I: Basic concepts. Tellus A 57, 219–233 (2005)
Uppala, S. et al. ERA-40: ECMWF 45-year reanalysis of the global atmosphere and surface conditions 1957–2002. ECMWF Newsl. 101, 2–21 (2004)
New, M., Hulme, M. & Jones, P. D. Representing twentieth century space-time climate variability. Part I: Development of a 1961–90 mean monthly terrestrial climatology. J. Clim. 12, 829–856 (1999)
New, M., Hulme, M. & Jones, P. Representing twentieth-century space-time climate variability. Part II: Development of 1901–96 monthly grids of terrestrial surface climate. J. Clim. 13, 2217–2238 (2000)
Mason, S. J. & Graham, N. E. Areas beneath the relative operating characteristics (ROC) and levels (ROL) curves: statistical significance and interpretation. Q. J.R. Meteorol. Soc. 128, 2145–2166 (2002)
We thank the WHO-AFRO Southern Africa Inter-Country Malaria Team (SAMC) in Zimbabwe and the National Malaria Control Unit in Botswana for enabling this study. The work reported here is part of the EU-funded DEMETER and ENSEMBLES projects. It was additionally supported financially by the UK Department for International Development through the Malaria Knowledge Programme, Liverpool School of Tropical Medicine, UK, and by a cooperative agreement from the US National Oceanic and Atmospheric Administration. The authors acknowledge considerable technical support from ECMWF staff and consultants, in particular from the Seasonal Forecast Section. The views herein contained are those of the authors and do not necessarily reflect the views of WHO, DfID, EU, NOAA or any of their sub-agencies.
Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.
About this article
Cite this article
Thomson, M., Doblas-Reyes, F., Mason, S. et al. Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature 439, 576–579 (2006). https://doi.org/10.1038/nature04503
Climate change and infectious disease research in Nepal: Are the available prerequisites supportive enough to researchers?
Acta Tropica (2020)
Scientific Reports (2020)
CMIP5: a Monte Carlo assessment of changes in summertime precipitation characteristics under RCP8.5-sensitivity to annual cycle fidelity, overconfidence, and gaussianity
Climate Dynamics (2020)
A bias-corrected projection for the changes in East Asian summer monsoon rainfall under global warming
Climate Dynamics (2020)