Susceptibility Risk Index Mapping of Population at Tuberculosis Epidemic Risk
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This paper presents the spatial susceptibility risk mapping of tuberculosis (TB) using a geographical information system (GIS) index model and satellite remote sensing (RS) imagery. GIS and RS-based index approach is proposed as an alternative method in identifying potentially high-risk areas in Klang, Selangor. The level of risk for the selected socio-spatial factors and the risk map was classified into five-scale from level 1, which is no risk to level 5, which indicates high risk by applying an overlay analysis and a weighted linear combination. The risk index map shows that a high concentration of TB cases is located in the district's urban and crowded areas.
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Copyright (c) 2023 Abdul Rauf Abdul Rasam, Wan Nor Syahirah Jumali, Ilham Abdul Jalil, Lalu Muhamad Jaelani
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Abdul Rasam , A. R. ., Mohd Shariff , N. ., Dony , J. ., & Ling , O. H. L. (2020). Local Spatial Knowledge for Eliciting Risk Factors and Disease Mapping of Tuberculosis Epidemics. Environment-Behaviour Proceedings Journal, 5(SI2), 45-51. https://doi.org/10.21834/ebpj.v5iSI2.2522
Abdul Rasam AR, Mohd Shariff N. Disease Mapping and Spatial Landscape Characterization of Tuberculosis Ecology in Malaysia. Abstr ICA. 2019
Abdul Rasam, A.R., Mohd Shariff, N., & Dony, J.F. (2019) Geospatial-Based Model for Diagnosing Potential High-Risk Areas of Tuberculosis Disease in Malaysia. MATEC Web of Conferences, 266, 26602007. https://doi.org/10.1051/matecconf/2019 I 2018 266 26602007
Abdul Rasam., A. R, Mohd Shariff, ., & Dony, J. F. (2016). Identifying High-Risk Populations of Tuberculosis Using Environmental Factors and GIS Based Multi-Criteria Decision Making Method. The International Archives ofc Photogrammetry, Remote Sensing and Spatial Information Science. Vol. XLII-4/W1 9-13. https://doi.org/10.5194/isprs-archives-XLII-4-W1-9-2016 , 2016
Alireza, S. (2022). The applications of MCDM methods in COVID-19 pandemic: A state of the art review. Applied Soft Computing, 126, 109238. https://doi.org/10.1016/j.asoc.2022.109238
Bishop, K., & Said, I., (2017). Challenges of Participatory Qualitative Research in a Malaysian and Australian Hospital. Asian Journal of Environment-Behaviour Studies, 2(4), 1-11.
Chang, K. (2018). Introduction to Geographic Information Systems, USA.McGraw Hill, 389-399
Couceiro, L., Santana, P., & Nunes, C. (2011). Pulmonary tuberculosis and risk factors in Portugal: a spatial analysis. The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease, 15(11), 1445–i. https://doi.org/10.5588/ijtld.10.0302
De Queiroga, RPF., de Sá, LD., Nogueira, J. de A., de Lima, ERV, Silva ACO., Pinheiro PGOD. Spatial distribution of tuberculosis and relationship with living conditions in an urban area of Campina Grande - 2004 to 2007. Rev Bras Epidemiol. (2012).
Feske, ML., Teeter LD, Musser JM, Graviss EA. (2011).Including the third dimension: A spatial analysis of TB cases in Houston Harris County. Tuberculosis. 2011;
Goswami ND, Hecker EJ, Vickery C, Ahearn MA, Cox GM, Holland DP, (2012). Geographic Information System based Screening for TB, HIV, and Syphilis (GIS-THIS): A Cross-Sectional Study. PLoS One.
Harling G, Castro MC (2014). A spatial analysis of social and economic determinants of tuberculosis in Brazil. Heal Place. 2014.
Jalil IA, Abdul Rasam AR. (2021). Disease Risk Mapping of Tuberculosis Hotspots in Klang: Where and Why the Areas Can Be at High Risk of Infection?. Malaysian Journal of Remote Sensing & GIS.
Lima SVMA, Dos Santos AD, Duque AM, De Oliveira Goes MA, Da Silva Peixoto MV, Da Conceição Araújo D, (2019). Spatial and temporal analysis of tuberculosis in an area of social inequality in Northeast Brazil. BMC Public Health. 2019;19(1):1–9.
Liu Y, Li X, Wang W, Li Z, Hou M, He Y, (2012). Investigation of space-time clusters and geospatial hot spots for the occurrence of tuberculosis in Beijing. Int J Tuberc Lung Dis.
Maciel, E. L., Pan, W., Dietze, R., Peres, R. L., Vinhas, S. A., Ribeiro, F. K., Palaci, M., Rodrigues, R. R., Zandonade, E., & Golub, J. E. (2010). Spatial patterns of pulmonary tuberculosis incidence and their relationship to socio-economic status in Vitoria, Brazil. The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease, 14(11), 1395–1402.
Maris, N.M.N., Mansor, S., & Shafri, H.Z.M. (2008). Apicultural site zonation using GIS and multi-criteria decision analysis. Pertanika J. Trop. Agric. Sci. 31(2): 147 - 162.
Mohammad, N. S., Abdul Rasam, A. R., Ghazali, R., Idris, R., and Abu Bakar, R. (2022). Spatial Clustering Phenomena Of Covid-19 Cases In Selangor: A Hotspot Analysis And Ordinary Least Squares Method, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W6-2022, 237–243. https://doi.org/10.5194/isprs-archives-XLVIII-4-W6-2022-237-2023 , 2023.
Nana Yakam A, Noeske J, Dambach P, Bowong S, Fono LA, Ngatchou-Wandji J. Spatial analysis of tuberculosis in Douala, Cameroon: Clustering and links with socio-economic status. Int J Tuberc Lung Dis. 2014.
Rajab, N. A., Hashim, N., Rasam, A. R. A. (2020). Spatial mapping and analysis of tuberculosis cases in Kuala Lumpur, Malaysia. Proceedings of the 2020 IEEE 10th International Conference on System Engineering and Technology (ICSET); November 2020; Shah Alam, Malaysia
Rakibul, A., & Md Mahbub, H. (2021). Leveraging GIS and spatial analysis for informed decision-making in the COVID-19 pandemic. Health Policy and Technology, 10 (1), 7-9. https://doi.org/10.1016/j.hlpt.2020.11.009.
Ridzuan, N., Rauf, A.R,A., Isa, M., & Shafie, F. (2021). Spatial Interaction between Lifestyles and Tuberculosis: An Expert and Public Participatory GIS in Malaysia. International Journal of Geoinformatics, 17(5), 178–192. https://doi.org/10.52939/ijg.v17i5.2033
Roza DL da, Caccia-Bava M do CGG, Martinez EZ.(2012). Spatio-temporal patterns of tuberculosis incidence in. Ribeirão Preto, State of São Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis. Rev Soc Bras Med Trop.
Sameer, S., & Sandy, A. (2023). GIS-based COVID-19 vulnerability mapping in the West Bank, Palestine. International Journal of Disaster Risk Reduction, 64, (102483), 2212-4209, https://doi.org/10.1016/j.ijdrr.2021.102483.
Soni, P., Gupta, I., Singh, P., Porte, D.S., & Kumar, D. (2022). GIS-based AHP analysis to recognize the COVID-19 concern zone in India. GeoJournal, 88(1), 1-13. https://doi: 10.1007/s10708-022-10605-8.
Tadesse T, Demissie M, Berhane Y, Kebede Y, Abebe M. (2013).The Clustering of Smear-Positive Tuberculosis in Dabat, Ethiopia: A Population Based Cross Sectional Study. PLoS One.
Tiwari, N., Adhikari, C., Tewari, A. (2006). Investigation of geo-spatial hotspots for the occurrence of tuberculosis in Almora district, India, using GIS and spatial scan statistic. Int J Health Geogr, 5, 33. https://doi.org/10.1186/1476-072X-5-33
Wang T, Xue F, Chen Y, Ma Y, Liu Y. (2012). The Spatial Epidemiology of Tuberculosis in Linyi. BMC Public Health.
Wong NS, Law CY, Lee MK, Lee SS, Lin H. (2007). An alert system for informing environmental risk of dengue infections. In: Lecture Notes in Geoinformation and Cartography.
World Health organization [WHO] (2023, March, 02). TB determinants. https://www.who.int/publications/digital/global-tuberculosis-report-2021/uhc-tb-determinants/determinants
Yazdani-Charati J, Siamian H, Kazemnejad A, Mohammad V. (2014). Spatial clustering of tuberculosis incidence in the North of Iran. Glob J Health Sci.
Zafar Ullah AN, Huque R, Husain A, Akter S, Akter H, Newell JN. (2012).Tuberculosis in the workplace: Developing partnerships with the garment industries in Bangladesh. Int J Tuberc Lung Dis.
Zaini, N.N.N., Rasam, A.R.A., & Ahmad, C.B. (2022). Socio-Economic Characteristics of Urban Tuberculosis Areas in Petaling, Selangor: A Current Spatial Exploratory Scenario. IOP Conf. Ser.: Earth Environ. Sci, 1067,012041. doi:10.1088/1755-1315/1067/1/012041