Journal of Geoscience and Environment Protection

Volume 5, Issue 11 (November 2017)

ISSN Print: 2327-4336   ISSN Online: 2327-4344

Google-based Impact Factor: 0.72  Citations  

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

HTML  XML Download Download as PDF (Size: 2827KB)  PP. 102-117  
DOI: 10.4236/gep.2017.511008    1,991 Downloads   5,193 Views  Citations

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.

Share and Cite:

Hassaan, M. , Abdrabo, M. and Masabarakiza, P. (2017) GIS-Based Model for Mapping Malaria Risk under Climate Change Case Study: Burundi. Journal of Geoscience and Environment Protection, 5, 102-117. doi: 10.4236/gep.2017.511008.

Cited by

[1] Geospatial clustering and hot spot detection of malaria incidence in Bahawalpur district of Pakistan
GeoJournal, 2022
[2] Downscaling future temperature and precipitation values in Kombolcha Town, South Wollo in Ethiopia
J Environ Hazards, 2021
[3] How Can Technology and Innovation Be Used to Alleviate the Climate Crisis in Developing Countries Through Mitigation and Adaptation?
2021
[4] Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique
2021
[5] Enquêtede mortalité rétrospective dansle district sanitaire de Ryansoro, province de Gitega, Burundi Rapport
2021
[6] Ausgewählte Meldungen und aktuelle Entwicklungen
2020
[7] Climate Change Trends, Projections and Vulnerability Integration to Enhance Urban Resilience Planning: The case of Addis Ababa City
2019
[8] Climate Change Trends, Projections and Vulnerability Integration
2019
[9] Geographic information system based malaria risk analysis and mapping in Erer District eastern Ethiopia
2019
[10] Downscaling of future temperature and precipitation extremes in Addis Ababa under climate change
Climate, 2018
[11] GIS based quantification and mapping of climate change vulnerability hotspots in Addis Ababa
2018
[12] Malaria prone area analysis and mapping using geospatial tools: The case of Amibara and Gewane Woreda, afar region, Ethiopia
Journal of Geography and Regional Planning, 2018

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.