Spatial analysis and mapping of malaria risk areas using multi-criteria decision making in Ibadan, Oyo state, Nigeria
Vol 6, Issue 2, 2023
VIEWS - 438 (Abstract) 59 (PDF)
Abstract
Malaria is a mosquito-borne infectious disease that affects humans and poses a severe public health problem. Nigeria has the highest number of global cases. Geospatial technology has been widely used to study the risks and factors associated with malaria hazards. The present study is conducted in Ibadan, Oyo State, Nigeria. The objective of this study is to map out areas that are at high risk of the prevalence of malaria by considering a good number of factors as criteria that determine the spread of malaria within Ibadan using open-source and Landsat remote sensing data and further analysis in GIS-based multi-criteria evaluation (MCE). This study considered factors like climate, environmental, socio-economic, and proximity to health centers as criteria for mapping malaria risk. The MCE used a weighted overlay of the factors to produce an element at-risk map, a malaria hazard map, and a vulnerability map. These maps were overlaid to produce the final malaria risk map, which showed that 72% of Ibadan has a risk of malaria prevalence. Identification and delineation of risk areas in Ibadan would help policymakers and decision-makers mitigate the hazards and improve the health status of the state.
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1. Wikipedia. Ibadan. Available online: https://en.wikipedia.org/wiki/Ibadan (accessed on 30 May 2023).
2. World Health Organization. World malaria report 2005. Available online: https://www.who.int/publications/i/item/9241593199 (accessed on 21 May 2023).
3. Walter K, John CC. Malaria. JAMA 2022; 327(6): 597. doi: 10.1001/jama.2021.21468
4. Caraballo H, King K. Emergency department management of mosquito-borne illness: Malaria, dengue, and west Nile virus. Emergency Medicine Practice 2014; 16(5): 1–23.
5. Centre for Disease Control and Prevention. Malaria’s impact worldwide. Available online: https://www.cdc.gov/malaria/malaria_worldwide/impact.html (accessed on 21 May 2023).
6. World Health Organization. World malaria report 2021. Available online: https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2021 (accessed on 21 May 2023).
7. Okunlola OA, Oyeyemi OT. Spatio-temporal analysis of the association between the incidence of malaria and environmental predictors of malaria transmission in Nigeria. Scientific Reports 2019; 9(1): 17500. doi: 10.1038/s41598-019-53814-x
8. Awosolu OB, Yahaya ZS, Haziqah MTF, et al. A cross-sectional study of the prevalence, density, and risk factors associated with malaria transmission in urban communities of Ibadan, southwestern Nigeria. Heliyon 2021; 7(1): e05975. doi: 10.1016/j.heliyon.2021.e05975
9. Wimberly MC, de Beurs KM, Loboda TV, Pan WK. Satellite observations and malaria: New opportunities for research and applications. Trends Parasitol 2021; 37(6): 525–537. doi: 10.1016/j.pt.2021.03.003
10. Kazansky Y, Wood D, Sutherlun J. The current and potential role of satellite remote sensing in the campaign against malaria. Acta Astronaut 2016; 121: 292–305. doi: 10.1016/j.actaastro.2015.09.021
11. Parselia E, Kontoes C, Tsouni A, et al. Satellite earth observation data in epidemiological modeling of malaria, dengue and west Nile virus: A scoping review. Remote Sensing 2019; 11(16): 1862. doi: 10.3390/rs11161862
12. Moss WJ, Hamapumbu H, Kobayashi T, et al. Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: A cross-sectional and longitudinal community survey. Malaria Journal 2011; 10(1): 163. doi: 10.1186/1475-2875-10-163
13. Midekisa A, Senay G, Henebry GM, et al. Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malaria Journal 2012; 11(1): 165. doi: 10.1186/1475-2875-11-165
14. Ebhuoma O, Gebreslasie M. Remote sensing-driven climatic/environmental variables for modeling malaria transmission in sub-saharan Africa. International Journal of Environmental Research and Public Health 2016; 13(6): 584. doi: 10.3390/ijerph13060584
15. Endo N, Eltahir EAB. Increased risk of malaria transmission with warming temperature in the Ethiopian highlands. Environmental Research Letters 2020; 15: 054006. doi: 10.1088/1748-9326/ab7520
16. Ahmad F, Goparaju L, Qayum A. Studying malaria epidemic for vulnerability zones: Multi-criteria approach of geospatial tools. Journal of Geoscience and Environment Protection 2017; 5(5): 30–53. doi: 10.4236/gep.2017.55003
17. Ahmed A. GIS and remote sensing for malaria risk mapping, Ethiopia. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives 2014; 40(8): 155–161. doi: 10.5194/isprsarchives-XL-8-155-2014
18. Alemayehu A. Biology and epidemiology of Plasmodium falciparum and Plasmodium vivax gametocyte carriage: Implication for malaria control and elimination. Parasite Epidemiol Control 2023; 21: e00295. doi: 10.1016/j.parepi. 2023.e00295
19. Khanam S. Prevalence and epidemiology of malaria in Nigeria: A review. International Journal of Research in Pharmacy and Biosciences 2017; 4(8): 10–12.
20. Akinbobola A, Ikiroma IA. Determining malaria hotspot using climatic variables and geospatial technique in central urban area of Ibadan, southwest, Nigeria. Journal of Climatol Weather Forecasting 2018; 6: 225. doi: 10.4172/2332-2594.1000225
21. Wimberly MC, Nekorchuk DM, Kankanala RR. Cloud-based applications for accessing satellite earth observations to support malaria early warning. Scientific Data 2022; 9: 208. doi: 10.1038/s41597-022-01337-y
22. Zhao X, Thanapongtharm W, Lawawirojwong S, et al. Malaria risk map using spatial multi-criteria decision analysis along Yunnan border during the pre-elimination period. American Journal of Tropical Medicine and Hygiene 2020; 103(2): 793–809. doi: 10.4269/ajtmh.19-0854
23. Oyo state news. Available online: https://oyoaffairs.net/category/news/2019 (accessed on 21 May 2023).
24. Patz JA, Olson SH. Malaria risk and temperature: Influences from global climate change and local land use practices. Proceedings of the National Academy of Sciences 2006; 103(15): 5635–5636. doi: 10.1073/pnas.0601493103
25. Ugwu CLJ, Zewotir T. Evaluating the effects of climate and environmental factors on under-5 children malaria spatial distribution using generalized additive models (GAMs). Journal of Epidemiol Glob Health 2020; 10(4): 304–314. doi: 10.2991/jegh.k.200814.001
26. Ra PK, Nathawat MS, Onagh M. Application of multiple linear regression model through GIS and remote sensing for malaria mapping in Varanasi District, India. Health Science Journal 2012; 6(4): 731–749.
27. Kaufmann C, Briegel H. Flight performance of the malaria vectors Anopheles gambiae and Anopheles atroparvus. Journal of Vector Ecology 2004; 29(1): 140–153.
28. World Health Organization. Vector-borne diseases. Available online: www.who.int (accessed on 21 May 2023).
29. Chikodzi D. Spatial modelling of malaria risk zones using environmental, anthropogenic variables and geographical information systems techniques. Journal of Geosciences and Geomatics 2013; 1: 8–14. doi: 10.12691/jgg-1-1-2
30. Shook G. An assessment of disaster risk and its management in Thailand. Disasters 1997; 21(1): 77–88. doi: 10.1111/1467-7717.00045
31. Greene R, Devillers R, Luther JE, Eddy BG. GIs‐based multiple‐criteria decision analysis. Geography Compass 2011; 5(6): 412–432. doi: 10.1111/j.1749-8198.2011.00431.x
32. GIS People. Multi-criteria analysis. Available online: https://www.gispeople.com.au/geospatial-consulting/multi-criteria-analysis/ (accessed on 21 May 2023).
33. Solanke OO, Taiwo AI, Oyewole O. Modeling the effects of climate variability on malaria prevalence. LAUTECH Journal of Engineering and Technology 2022; 16(2): 137–144.
34. Ekpa DE, Salubi EA, Olusola JA, Akintade D. Spatio-temporal analysis of environmental and climatic factors impacts on malaria morbidity in Ondo State, Nigeria. Heliyon 2023; 9(3): e14005. doi: 10.1016/j.heliyon.2023.e14005
35. Santos-Vega M, Bouma MJ, Kohli V, Pascual M. Population density, climate variables, and poverty synergistically structure spatial risk in urban malaria in India. PLoS Neglected Tropical Diseases 2016; 10(12): e0005155. doi: 10.1371/journal.pntd.0005155
36. Gebre SL, Temam N, Regassa A. Spatial analysis and mapping of malaria risk areas using multi-criteria decision making in Didessa district, south west Ethiopia. Cogent Environmental Science 2020; 6(1): 1860451. doi: 10.1080/23311843.2020.1860451
37. UN research ranks Ibadan as 2nd fastest growing city in 2022. Available online: https://oyoaffairs.net/un-research-ranks-ibadan-as-2nd-fastest-growing-city-in-2022/ (accessed on 21 May 2023).
38. Wilson ML, Krogstad DJ, Arinaitwe E, et al. Urban malaria: Understanding its epidemiology, ecology, and transmission across seven diverse ICEMR network sites. American Journal of Tropical Medicine and Hygiene 2015; 93: 110–123. doi: 10.4269/ajtmh.14-0834
39. Toh KB, Millar J, Psychas P, et al. Guiding placement of health facilities using multiple malaria criteria and an interactive tool. Malaria Journal 2021; 20: 455. doi: 10.1186/s12936-021-03991-w
40. How weighted overlay works. Available online: https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-weighted-overlay-works.html (accessed on 21 May 2023).
DOI: https://doi.org/10.24294/jgc.v6i2.2214
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