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Article citations


Tonnang, H.E., Kangalawe, R.Y. and Yanda, P.Z. (2010) Predicting and Mapping Malaria under Climate Change Scenarios: The Potential Redistribution of Malaria Vectors in Africa. Malaria Journal, 9, 111.

has been cited by the following article:

  • TITLE: GIS-Based Model for Mapping Malaria Risk under Climate Change Case Study: Burundi

    AUTHORS: Mahmoud A. Hassaan, Mohamed A. Abdrabo, Prosper Masabarakiza

    KEYWORDS: Malaria Risk, GIS, Mapping Vulnerability, Burundi

    JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.5 No.11, November 13, 2017

    ABSTRACT: Malaria is one of the largest problems threatening global public health that is expected to increase in the future under climate change due to associated warming and wetter conditions. This will exacerbate disease burden in Burundi as one of sub-Saharan African countries, where 2 million cases of malaria were reported in 2015. This highlights the need for developing a methodology for mapping malaria risk under climate change and delineating those regions that may potentially experience malaria epidemics in the future. Malaria transmission and distribution are generally determined by a wide range of climatic, topographic and socioeconomic factors. The paper in hand is intended to map malaria risk in Burundi under climate change up to 2050. For this purpose, a GIS-based model was developed for mapping malaria as a function of various climatic and topographic determinants of malaria. The developed GIS-model was used in mapping malaria risk under current climatic conditions. Thereafter, the produced risk map was validated compared to malaria morbidity data in Burundi at health district level. Finally, the GIS-model was applied to map malaria risk in the future under RCPs 2.6 and 8.5 scenarios up to 2050. It was found that about 34.6% and 44% of Burundi land surface will be highly vulnerable to malaria risk by 2050 under RCPs 2.6 and 8.5 scenario, respectively. Also, it was noted that such highly vulnerable areas are distributed mainly in northern parts of the country. The suggested GIS-based model for mapping malaria risk under climate change can contribute largely to more informed decision-making and policy making process in terms of planning for intervention and control malaria risk. This in turn can support reducing disease burden and improving resilience to climate change.