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

  • Joseph Leedale | j.leedale@liverpool.ac.uk School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom.
  • Adrian M. Tompkins Abdus Salam International Centre for Theoretical Physics, Trieste, Italy.
  • Cyril Caminade Department of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom.
  • Anne E. Jones Department of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom.
  • Grigory Nikulin Rossby Centre, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden.
  • Andrew P. Morse School of Environmental Sciences, University of Liverpool, Liverpool; National Institute for Health Research, Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, United Kingdom.

Abstract

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.

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Published
2016-03-31
Section
Original Articles
Keywords:
Malaria, Climate change, Vector-borne disease, Climate model ensemble, Eastern Africa
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How to Cite
Leedale, J., Tompkins, A., Caminade, C., Jones, A., Nikulin, G., & Morse, A. (2016). Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty. Geospatial Health, 11(1s). https://doi.org/10.4081/gh.2016.393

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