Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty

Submitted: 24 June 2015
Accepted: 22 December 2015
Published: 31 March 2016
Abstract Views: 3751
PDF: 1513
HTML: 2523
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.



PlumX Metrics


Download data is not yet available.


How to Cite

Leedale, J., Tompkins, A. M., Caminade, C., Jones, A. E., Nikulin, G., & Morse, A. P. (2016). Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty. Geospatial Health, 11(s1).

List of Cited By :

Crossref logo