The online version of this article (doi:10.1186/1475-2875-11-331) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
SE is project co-lead for the spatiotemporal epidemiological modeller; he developed the models, ran the simulations, and contributed to data analysis. MD assisted with model design, GIS data processing and data analysis. JVD performed literature reviews and assisted in writing and editing of the manuscript. AK advised contributed to the vector population model and the extended MacDonald Ross Model. NW conducted a preliminary review of the vector capacity scientific literature and helped develop the vector population model. JL reviewed the research design and advised on statistical analytical techniques. JHK directed the research, led the data analysis, and wrote the final paper. SE and JL performed critical review of the final manuscript. All authors read and approved the final manuscript.
The role of the Anopheles vector in malaria transmission and the effect of climate on Anopheles populations are well established. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic. Measurement of malaria response to fluctuations in climate variables offers a way to address these difficulties. Given the demonstrated sensitivity of malaria transmission to vector capacity, this work tests response functions to fluctuations in land surface temperature and precipitation.
This study of regional sensitivity of malaria incidence to year-to-year climate variations used an extended Macdonald Ross compartmental disease model (to compute malaria incidence) built on top of a global Anopheles vector capacity model (based on 10 years of satellite climate data). The predicted incidence was compared with estimates from the World Health Organization and the Malaria Atlas. The models and denominator data used are freely available through the Eclipse Foundation’s Spatiotemporal Epidemiological Modeller (STEM).
Although the absolute scale factor relating reported malaria to absolute incidence is uncertain, there is a positive correlation between predicted and reported year-to-year variation in malaria burden with an averaged root mean square (RMS) error of 25% comparing normalized incidence across 86 countries. Based on this, the proposed measure of sensitivity of malaria to variations in climate variables indicates locations where malaria is most likely to increase or decrease in response to specific climate factors. Bootstrapping measures the increased uncertainty in predicting malaria sensitivity when reporting is restricted to national level and an annual basis. Results indicate a potential 20x improvement in accuracy if data were available at the level ISO 3166–2 national subdivisions and with monthly time sampling.
The high spatial resolution possible with state-of-the-art numerical models can identify regions most likely to require intervention due to climate changes. Higher-resolution surveillance data can provide a better understanding of how climate fluctuations affect malaria incidence and improve predictions. An open-source modelling framework, such as STEM, can be a valuable tool for the scientific community and provide a collaborative platform for developing such models.
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- A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence
Judith V Douglas
James H Kaufman
- BioMed Central
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