Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty
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 projecti...
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| Format: | Article |
| Language: | English |
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PAGEPress Publications
2016-03-01
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| Series: | Geospatial Health |
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| Online Access: | http://geospatialhealth.net/index.php/gh/article/view/393 |
| _version_ | 1818417449384542208 |
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| author | Joseph Leedale Adrian M. Tompkins Cyril Caminade Anne E. Jones Grigory Nikulin Andrew P. Morse |
| author_facet | Joseph Leedale Adrian M. Tompkins Cyril Caminade Anne E. Jones Grigory Nikulin Andrew P. Morse |
| author_sort | Joseph Leedale |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T12:06:57Z |
| format | Article |
| id | doaj.art-a5fcb59c7fac4ffc8f5898f7bf379942 |
| institution | Directory Open Access Journal |
| issn | 1827-1987 1970-7096 |
| language | English |
| last_indexed | 2024-12-14T12:06:57Z |
| publishDate | 2016-03-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Geospatial Health |
| spelling | doaj.art-a5fcb59c7fac4ffc8f5898f7bf3799422022-12-21T23:01:51ZengPAGEPress PublicationsGeospatial Health1827-19871970-70962016-03-01111s10.4081/gh.2016.393353Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertaintyJoseph Leedale0Adrian M. Tompkins1Cyril Caminade2Anne E. Jones3Grigory Nikulin4Andrew P. Morse5School of Environmental Sciences, University of Liverpool, LiverpoolAbdus Salam International Centre for Theoretical Physics, TriesteDepartment of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, LiverpoolDepartment of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, LiverpoolRossby Centre, Swedish Meteorological and Hydrological Institute, NorrköpingSchool of Environmental Sciences, University of Liverpool, Liverpool; National Institute for Health Research, Health Protection Research Unit in Emerging and Zoonotic Infections, LiverpoolThe 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.http://geospatialhealth.net/index.php/gh/article/view/393MalariaClimate changeVector-borne diseaseClimate model ensembleEastern Africa |
| spellingShingle | Joseph Leedale Adrian M. Tompkins Cyril Caminade Anne E. Jones Grigory Nikulin Andrew P. Morse Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty Geospatial Health Malaria Climate change Vector-borne disease Climate model ensemble Eastern Africa |
| title | Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty |
| title_full | Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty |
| title_fullStr | Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty |
| title_full_unstemmed | Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty |
| title_short | Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty |
| title_sort | projecting malaria hazard from climate change in eastern africa using large ensembles to estimate uncertainty |
| topic | Malaria Climate change Vector-borne disease Climate model ensemble Eastern Africa |
| url | http://geospatialhealth.net/index.php/gh/article/view/393 |
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