DOI: 10.1007/s10584-017-1996-y
Scopus记录号: 2-s2.0-85025064342
论文题名: Prediction of future malaria hotspots under climate change in sub-Saharan Africa
作者: Semakula H.M. ; Song G. ; Achuu S.P. ; Shen M. ; Chen J. ; Mukwaya P.I. ; Oulu M. ; Mwendwa P.M. ; Abalo J. ; Zhang S.
刊名: Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480
出版年: 2017
卷: 143, 期: 2018-03-04 起始页码: 415
结束页码: 428
语种: 英语
英文关键词: Bayesian belief networks
; Children
; Climate change
; GIS
; Malaria
; Sub-Saharan Africa
Scopus关键词: Bayesian networks
; Climate models
; Complex networks
; Diseases
; Geographic information systems
; Malaria control
; Children
; Climatic factors
; Coupled modeling
; Integrated models
; Malaria
; Percentage points
; Probabilistic maps
; Sub-saharan africa
; Climate change
; Bayesian analysis
; cause of death
; child health
; climate change
; disease control
; disease prevalence
; future prospect
; GIS
; malaria
; network analysis
; prediction
; Sub-Saharan Africa
; West Africa
英文摘要: Malaria is a climate sensitive disease that is causing rampant deaths in sub-Saharan Africa (SSA) and its impact is expected to worsen under climate change. Thus, pre-emptive policies for future malaria control require projections based on integrated models that can accommodate complex interactions of both climatic and non-climatic factors that define malaria landscape. In this paper, we combined Geographical Information System (GIS) and Bayesian belief networks (BBN) to generate GIS-BBN models that predicted malaria hotspots in 2030, 2050 and 2100 under representative concentration pathways (RCPs) 4.5 and 8.5. We used malaria data of children of SSA, gridded environmental and social-economic data together with projected climate data from the 21 Coupled Model Inter-comparison Project Phase 5 models to compile the GIS-BBN models. Our model on which projections were made has an accuracy of 80.65% to predict the high, medium, low and no malaria prevalence categories correctly. The non-spatial BBN model projection shows a moderate variation in malaria reduction for the high prevalence category among RCPs. Under the low prevalence category, an increase in malaria is seen but with little variation ranging between 4.6 and 5.6 percentage points. Spatially, under RCP 4.5, most parts of SSA will have medium malaria prevalence in 2030, while under RCP 8.5, most parts will have no malaria except in the highlands. Our BBN-GIS models show an overall shift of malaria hotspots from West Africa to the eastern and southern parts of Africa especially under RCP 8.5. RCP 8.5 will not expand the high and medium malaria prevalence categories in all the projection years. The generated probabilistic maps highlight future malaria hotspots under climate change on which pre-emptive policies can be based. © 2017, Springer Science+Business Media Dordrecht. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/83962
Appears in Collections: 气候减缓与适应 气候变化事实与影响
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作者单位: Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, China; Faculty of Environment and Natural Resources, Albert Ludwigs University, Freiburg, Germany; Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing, China; Department of Geography, Geo-informatics and Climatic Sciences, Makerere University, Kampala, Uganda; Human Ecology Division, Lund University, Lund, Sweden; Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya; Department of Health Promotion and Development, University of Bergen, Bergen, Norway
Recommended Citation:
Semakula H.M.,Song G.,Achuu S.P.,et al. Prediction of future malaria hotspots under climate change in sub-Saharan Africa[J]. Climatic Change,2017-01-01,143(2018-03-04)