Email updates

Keep up to date with the latest news and content from Malaria Journal and BioMed Central.

This article is part of the supplement: Challenges in malaria research

Open Access Poster presentation

Development of dynamical weather-disease models to project and forecast malaria in Africa

Volker Ermert1*, Andreas H Fink1, Andrew P Morse2, Anne E Jones2, Heiko Paeth3, Francesca Di Giuseppe4 and Adrian M Tompkins5

  • * Corresponding author: Volker Ermert

Author Affiliations

1 Institute of Geophysics and Meteorology, University of Cologne, Cologne, Germany

2 School of Environmental Sciences, University of Liverpool, Liverpool, UK

3 Institute of Geography, University of Würzburg, Würzburg, Germany

4 European Centre for Medium-range Weather Forecasts (ECMWF), Reading, UK

5 Earth System Physics, Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

For all author emails, please log on.

Malaria Journal 2012, 11(Suppl 1):P133  doi:10.1186/1475-2875-11-S1-P133

The electronic version of this article is the complete one and can be found online at: http://www.malariajournal.com/content/11/S1/P133


Published:9 November 2012

© 2012 Ermert et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background

Weather and climate play an important role in the spread of malaria. Suitable weather conditions for malaria are found in sub-Saharan Africa, where most of the worldwide malaria cases and deaths are found. For this reason, integrated weather-disease malaria models are useful tools to project the malaria future and to provide monthly-to-seasonal forecasts.

Methods

Malaria projections and forecasts are undertaken by two dynamical mathematical-biological malaria models: (i) the LMM (Liverpool Malaria Model) [1-3] and (ii) VECTRI (VECtor-borne disease community model of the International Centre for Theoretical Physics, TRIeste). Both models are driven by daily temperature and precipitation values. An improved version of the LMM was introduced by [2], which was calibrated by malaria field observations from West Africa [3]. Regarding the assessment of the impact of climate change on malaria [4], the LMM was driven by data from the REgional MOdel (REMO) including the effect of land surface changes.

For the QWeCI (Quantifying Weather and Climate Impacts on health in developing countries) project, a seamless weather prediction system has been developed at ECMWF by appending the first 25 days of the monthly forecasting system with the Seasonal Forecasting System 4 to provide a continuous 120 day lead time prediction. The forecast is calibrated to correct for displacement errors of West African monsoonal precipitation.

Results and outlook

The malaria projections up to 2050 [4] based on the integrated REMO-LMM reveal a southward shift of the epidemic malaria area in West Africa due to the precipitation decline. The increased temperatures lead to an increase of transmission in highland territories. Formerly, malaria free areas become epidemic, whereas the epidemic risk is decreased in lower-altitude regions.

Actual research within the EU Seventh Framework Programme (FP7) QWeCI and HEALTHY FUTURES projects is underway to exploit the feasibility of monthly-to-seasonal malaria forecasts. QWeCI is currently developing prototype seamless malaria forecasts for Malawi (http://nwmstest.ecmwf.int/products/forecasts/d/inspect/catalog/research/qweci/ webcite).

The LMM and VECTRI neglect various important malaria factors like immunity, malaria control activities, or different vector characteristics. Further development of VECTRI will be undertaken to include other relevant malaria factors. Note that VECTRI represents a community model meaning that the model and code is publicly available (http://users.ictp.it/~tompkins/vectri/ webcite). The LMM is included in the so-called Disease Model Cradle (DMC) that is downloadable (http://www.liv.ac.uk/qweci/project_outputs/ webcite). Open-access web versions of both models are applicable for point data (see http://qweci.uni-koeln.de webcite).

References

  1. Hoshen MB, Morse AP: A weather-driven model of malaria transmission.

    Malar J 2004, 3:32. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  2. Ermert V, Fink AH, Jones AE, Morse AP: Development of a new version of the Liverpool Malaria Model. I. Refining the parameter settings and mathematical formulation of basic processes based on a literature review.

    Malar J 2011, 10:35. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  3. Ermert V, Fink AH, Jones AE, Morse AP: Development of a new version of the Liverpool Malaria Model. II. Calibration and validation for West Africa.

    Malar J 2011, 10:62. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  4. Ermert V, Fink AH, Morse AP, Paeth H: The Impact of Regional Climate Change on Malaria Risk due to Greenhouse Forcing and Land-Use Changes in Tropical Africa.

    EHP 2012, 120:77-84. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL