Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Malaria early warnings based on seasonal climate forecasts from multi-model ensembles

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Time series of the one-month lead December–February predicted SST averaged over the tropical Pacific region Niño3.4.
Figure 2: Composites of austral summer precipitation for central and southern Africa as a function of the standardized malaria annual incidence for Botswana.
Figure 3: Relationship between standardized log malaria annual incidence and summer precipitation for Botswana.
Figure 4: Forecast probability distribution function of standardized log malaria annual incidence for Botswana.

Similar content being viewed by others

References

  1. 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

    Google Scholar 

  2. 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

    Google Scholar 

  3. 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)

    Article  CAS  Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. Greenwood, B. M., Bojang, K., Whitty, C. & Targett, G. A. T. Malaria. Lancet 365, 1487–1498 (2005)

    Article  CAS  Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. 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

    Google Scholar 

  8. 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)

    Article  ADS  Google Scholar 

  9. Palmer, T. N. et al. Representing model uncertainty in weather and climate prediction. Annu. Rev. Earth Planet. Sci. 33, 163–193 (2005)

    Article  ADS  MathSciNet  CAS  Google Scholar 

  10. Goddard, L. et al. Current approaches to seasonal-to-interannual climate predictions. Int. J. Climatol. 21, 1111–1152 (2001)

    Article  Google Scholar 

  11. 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)

    Article  ADS  CAS  Google Scholar 

  12. Cane, M., Eshel, G. & Buckland, R. Forecasting Zimbabwean maize yield using eastern equatorial Pacific sea surface temperature. Nature 370, 204–205 (1994)

    Article  ADS  Google Scholar 

  13. 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)

    Article  ADS  Google Scholar 

  14. 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)

    Google Scholar 

  15. Richardson, D. S. Skill and relative economic value of the ECMWF ensemble prediction system. Q. J.R. Meteorol. Soc. 126, 649–667 (2000)

    Article  ADS  Google Scholar 

  16. 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)

  17. Brinkmann, U. & Brinkmann, A. Malaria and health in Africa—the present situation and epidemiologic trends. Trop. Med. Parasitol. 42, 204–213 (1991)

    CAS  PubMed  Google Scholar 

  18. Mason, S. J. & Tyson, P. D. in Droughts Vol. 1 A Global Assessment (ed. Wilhite, D. A.) 113–134 (Routledge, New York, 2000)

    Google Scholar 

  19. Xie, P. & Arkin, P. A. Global monthly precipitation from satellite-observed outgoing longwave radiation. J. Clim. 11, 137–164 (1998)

    Article  ADS  Google Scholar 

  20. 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)

    ADS  Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Article  ADS  Google Scholar 

  23. 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)

    Article  ADS  Google Scholar 

  24. 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)

    Article  ADS  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to T. N. Palmer.

Ethics declarations

Competing interests

Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

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

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature04503

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing