气候变化领域集成服务门户(CCPortal): Prediction of future malaria hotspots under climate change in sub-Saharan Africa

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DOI	10.1007/s10584-017-1996-y
	Prediction of future malaria hotspots under climate change in sub-Saharan Africa
	Semakula, Henry Musoke; Song, Guobao; Achuu, Simon Peter; Shen, Miaogen; Chen, Jingwen; Mukwaya, Paul Isolo; Oulu, Martin; Mwendwa, Patrick Mwanzia; Abalo, Jannette; Zhang, Shushen
通讯作者	Song, GB (通讯作者)
发表日期	2017
ISSN	0165-0009
EISSN	1573-1480
起始页码	415
结束页码	428
卷号	143 期号:3-4
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.
关键词	PLASMODIUM-FALCIPARUM ENVIRONMENTAL-MANAGEMENT BAYESIAN NETWORK SOURCE REDUCTION RANGE SHIFTS TRANSMISSION RISK ELIMINATION IMPACT UNCERTAINTY
英文关键词	Bayesian belief networks; GIS; Children; Climate change; Malaria; Sub-Saharan Africa
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000407170600010
来源期刊	CLIMATIC CHANGE
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259090
推荐引用方式 GB/T 7714	Semakula, Henry Musoke,Song, Guobao,Achuu, Simon Peter,et al. Prediction of future malaria hotspots under climate change in sub-Saharan Africa[J]. 中国科学院青藏高原研究所,2017,143(3-4).
APA	Semakula, Henry Musoke.,Song, Guobao.,Achuu, Simon Peter.,Shen, Miaogen.,Chen, Jingwen.,...&Zhang, Shushen.(2017).Prediction of future malaria hotspots under climate change in sub-Saharan Africa.CLIMATIC CHANGE,143(3-4).
MLA	Semakula, Henry Musoke,et al."Prediction of future malaria hotspots under climate change in sub-Saharan Africa".CLIMATIC CHANGE 143.3-4(2017).