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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 LU ; Mwendwa, Patrick Mwanzia; Abalo, Jannette and Zhang, Shushen (2017) In Climatic Change 143(3-4). p.415-428
Abstract

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... (More)

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.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bayesian belief networks, Children, Climate change, GIS, Malaria, Sub-Saharan Africa
in
Climatic Change
volume
143
issue
3-4
pages
415 - 428
publisher
Springer
external identifiers
  • scopus:85025064342
  • wos:000407170600010
ISSN
0165-0009
DOI
10.1007/s10584-017-1996-y
language
English
LU publication?
yes
id
8b8e13fc-54e0-4574-b45f-5d3e89dc3987
date added to LUP
2017-07-31 12:45:39
date last changed
2018-02-19 02:14:24
@article{8b8e13fc-54e0-4574-b45f-5d3e89dc3987,
  abstract     = {<p>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.</p>},
  author       = {Semakula, Henry Musoke and Song, Guobao and Achuu, Simon Peter and Shen, Miaogen and Chen, Jingwen and Mukwaya, Paul Isolo and Oulu, Martin and Mwendwa, Patrick Mwanzia and Abalo, Jannette and Zhang, Shushen},
  issn         = {0165-0009},
  keyword      = {Bayesian belief networks,Children,Climate change,GIS,Malaria,Sub-Saharan Africa},
  language     = {eng},
  number       = {3-4},
  pages        = {415--428},
  publisher    = {Springer},
  series       = {Climatic Change},
  title        = {Prediction of future malaria hotspots under climate change in sub-Saharan Africa},
  url          = {http://dx.doi.org/10.1007/s10584-017-1996-y},
  volume       = {143},
  year         = {2017},
}