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Prediction of future malaria hotspots under climate change in sub-Saharan Africa

Author

Listed:
  • Henry Musoke Semakula

    (Dalian University of Technology)

  • Guobao Song

    (Dalian University of Technology)

  • Simon Peter Achuu

    (Albert Ludwigs University)

  • Miaogen Shen

    (Chinese Academy of Sciences)

  • Jingwen Chen

    (Dalian University of Technology)

  • Paul Isolo Mukwaya

    (Makerere University)

  • Martin Oulu

    (Lund University)

  • Patrick Mwanzia Mwendwa

    (Jomo Kenyatta University of Agriculture and Technology)

  • Jannette Abalo

    (University of Bergen)

  • Shushen Zhang

    (Dalian University of Technology)

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 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.

Suggested Citation

  • Henry Musoke Semakula & Guobao Song & Simon Peter Achuu & Miaogen Shen & Jingwen Chen & Paul Isolo Mukwaya & Martin Oulu & Patrick Mwanzia Mwendwa & Jannette Abalo & Shushen Zhang, 2017. "Prediction of future malaria hotspots under climate change in sub-Saharan Africa," Climatic Change, Springer, vol. 143(3), pages 415-428, August.
    • Handle: RePEc:spr:climat:v:143:y:2017:i:3:d:10.1007_s10584-017-1996-y
    DOI: 10.1007/s10584-017-1996-y
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    References listed on IDEAS

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    1. Peter W. Gething & David L. Smith & Anand P. Patil & Andrew J. Tatem & Robert W. Snow & Simon I. Hay, 2010. "Climate change and the global malaria recession," Nature, Nature, vol. 465(7296), pages 342-345, May.
    2. Marcot, Bruce G., 2012. "Metrics for evaluating performance and uncertainty of Bayesian network models," Ecological Modelling, Elsevier, vol. 230(C), pages 50-62.
    3. Teresa K. Yamana & Arne Bomblies & Elfatih A. B. Eltahir, 2016. "Climate change unlikely to increase malaria burden in West Africa," Nature Climate Change, Nature, vol. 6(11), pages 1009-1013, November.
    4. Arne Bomblies, 2012. "Modeling the role of rainfall patterns in seasonal malaria transmission," Climatic Change, Springer, vol. 112(3), pages 673-685, June.
    5. Jurg Utzinger & Yesim Tozan & Burton H. Singer, 2001. "Efficacy and cost-effectiveness of environmental management for malaria control," Working Papers 266, Princeton University, Woodrow Wilson School of Public and International Affairs, Center for Health and Wellbeing..
    6. repec:pri:cheawb:malaria is not listed on IDEAS
    7. Detlef Vuuren & Jae Edmonds & Mikiko Kainuma & Keywan Riahi & Allison Thomson & Kathy Hibbard & George Hurtt & Tom Kram & Volker Krey & Jean-Francois Lamarque & Toshihiko Masui & Malte Meinshausen & N, 2011. "The representative concentration pathways: an overview," Climatic Change, Springer, vol. 109(1), pages 5-31, November.
    8. repec:pri:cheawb:malaria.pdf is not listed on IDEAS
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    1. Mazni Baharom & Norfazilah Ahmad & Rozita Hod & Fadly Syah Arsad & Fredolin Tangang, 2021. "The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review," IJERPH, MDPI, vol. 18(21), pages 1-22, October.
    2. Semakula, Henry Musoke & Liang, Song & Mukwaya, Paul Isolo & Mugagga, Frank, 2023. "Application of a Bayesian network modelling approach to predict the cascading effects of COVID-19 restrictions on the planting activities of smallholder farmers in Uganda," Agricultural Systems, Elsevier, vol. 211(C).

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