Main Article Content
This paper examined the vulnerability of Enugu State to compound risk in its healthcare system from a geospatial perspective. In particular, this paper applied the Multi-Criteria Evaluation (MCE) method using ArcGIS 10.7 software to demonstrate how GIS could be applied in public health management, using seven datasets. The information from these datasets was used to reclassify them into three levels of risk: high, moderate and low risk. The weighted overlay tool in ArcGIS 10.7 was used to assign weight values to the datasets according to their risk components to generate the compound risk map. Results revealed great disparities in the risk levels of the datasets across the wards. It also shows that 37.5%, 56.1% and 6.4% of wards in the study area were at high, moderate and low compound risk in their health system. This paper advocated that the drivers of vulnerability and exposure to healthcare risk should be addressed.
Wong G, Liu W, Liu Y, Zhou B, Bi Y, Gao GF. MERS, SARS, and Ebola: The role of Super-Spreaders in infectious disease. Cell Host & Microbe. 2015;18(4):398–401.
World Health Organization: Naming the coronavirus disease (COVID-19) and the virus that causes it; 2020. Accessed December 2020. Available:https://www.who.int/emergencies/diseases/novelcoronavirus-2019/technical-guidance/naming-thecoronavirus-disease-(covid-2019)-and-the-virusthat-causes-it
Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID 19 in real time. Lancet Infect Dis. 2020;3099(20):533-534.
Van Elsland SL, O'Hare R. Coronavirus pandemic could have caused 40 million deaths if left unchecked, Imperial College London; 2020. Accessed December 2020. Available:https://www.imperial.ac.uk/news/196496/coronavirus-pandemic-could-have-caused-40/
Worldmeter: Population; 2021. Accessed March 2021. Available:https://www.worldometers.info/coronavirus/
JHU: COVID 19 Dashboard Center for System Science and Engineering. John Hopkins University; Accessed December 2020.
Ohia C, Bakarey AS, Ahmad T. COVID-19 and Nigeria: Putting the realities in context. Inte J Inf Dis. 2020;95:279–281.
NCDC. Covid 19 in Nigeria: A situation report; 2021. Accessed December 2020. Available:https://ncdc.gov.ng/diseases/sitreps/?cat=14&name=An%20update%20of%20COVID 19%20outbreak%20in%20NigeriaData
NCDC: Covid 19 in Nigeria; 2020. Accessed December 2020. Available:https://covid19.ncdc.gov.ng/
Eze J. More ‘strange deaths’ reported in Enugu as yellow fever spreads. Premium Times; 2020. Accessed December 2020. Available:https://www.premiumtimesng.com/news/headlines/426406-more-strange-deaths-reported-in-enugu-as-yellow-fever-spreads.html
Yalcin M. Mapping the global spatio-temporal dynamics of COVID- 19 outbreak using cartograms during the first 150 days of the pandemic. Geocarto Int; 2020. DOI: https://doi.org/10.1080/10106049.2020.1844310
Kanga S, Sudhanshu, Meraj G, Farooq M, Nathawat MS, Singh SK. Reporting the management of COVID-19 threat in India using remote sensing and GIS based approach. Geocarto Int; 2020. DOI: https://doi.org/10.1080/10106049.2020.1778106
Mollalo A, Vahedi B, Rivera KM. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Sci Total Environ. 2020;728:138884.
Zhou C, Su F, Pei T, Zhang A, Du Y, Luo B, et al. COVID-19: challenges to GIS with big data. Geogr and Sustain. 2020;1(1):77–87.
Gorny AW, Bagdasarian N, Koh AHK, Lim YC, Ong JSM, Ng BSW, et al. SARS-CoV-2 in migrant worker dormitories: Geospatial epidemiology supporting outbreak management. Int Journal of Infectious Diseases. 2021;103:389-394.
Osayomi T, Adeleke R, Taiwo OJ, Gbadegesin AS, Fatayo OC, Akpoterai LE, et al. Cross-national variations in COVID-19 outbreak in West Africa: Where does Nigeria stand in the pandemic?. Spat. Inf. Res; 2020. Available:https://doi.org/10.1007/s41324-020-00371-5
Iyanda AE, Boakye KA, Lu Y, Oppong JR. Racial/Ethnic Heterogeneity and Rural-Urban Disparity of COVID-19 Case Fatality Ratio in the USA: A Negative Binomial and GIS-Based Analysis. Journal of Racial and Ethnic Health Disparities; 2021. Available:https://doi.org/10.1007/s40615-021-01006-7
Mbagwu JPC, Madububa BI, Agor MO, Ozuomba JO, Udoye MC. The Trend of COVID-19 in Nigeria. Journal of Applied Sciences Research. 2020;16(4):43-51.
Muhammad DG, Gbonjubola YT. Covid-19 in Nigeria: where are we? Yen Med J. 2021;3(1):39–46.
Farouq IS, Sulong Z, Sambo NU. The economic implication of COVID-19 in Nigeria. J of Critical Reviews. 2020;7(15): 2214-2218.
Jean K, Raad H, Gaythorpe KAM, Hamlet A, Mueller JE, Hogan D, et al. Assessing the impact of preventive mass vaccination campaigns on yellow fever outbreaks in Africa: A population level self-controlled case series study. PLoS Med 2021;18(2): e1003523. Available:https://doi.org/10.1371/journal.pmed.1003523
Shearer FM, Longbottom J, Browne AJ, Pigott DM, Brady OJ, Kraemer MUG, et al. Existing and potential infection risk zones of yellow fever worldwide: A modelling analysis. Lancet Glob Health. 2018;6:e270– 78.
Wisner B, Blaikie P,Cannon T, David I. At Risk: Natural Hazards, People's Vulnerability and Disasters. Routledge; 2004.
City Population. Enugu State,Nigeria: Population, statistics, charts, map and location; 2016. Accessed March 2021. Available:https://www.citypopulation.de/php/nigeria-admin.php?adm1id=NGA014
IPCC, Climate Change 2014: Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Geneva (Switzerland)]: IPCC; 2014. Accessed March 2021. Available:https://www.ipcc.ch/site/assets/uploads/2018/02/AR5_SYR_FINAL_SPM.pdf
BhuiyenC, Singh RP, Kigan F. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation. 2006;8(4):289-302.
Njoku CG, Efiong J, Uzoezie AC, Okeniyi FO, Alagbe AO. A GIS multi-criteria evaluation for flood risk-vulnerability mapping of Ikom local government area, cross river state. J Geogr Environ Earth Sci Int. 2018; 15(2):1-17.
Gayawan E, Awe OO, Oseni BM, Uzochukwu IC, Adekunle A, Samuel G, Eisen DP, Adegboye OA. The spatiotemporal epidemic dynamics of COVID-19 outbreak in Africa. Epidemiology and Infection. 2020;148(e212): 1–11. Available:https://doi.org/10.1017/S0950268820001983
Udenta J. Local Government and the Challenges to Primary Healthcare Delivery in Enugu State East Local Government Area Nigeria. Int J of Academic Management Science Research. 2018;2(11):18-33.
Uguru N, Onwujekwe O, Uguru CC, Ogu UU. Achieving universal health coverage in Nigeria: The dilemma of accessing dental care in Enugu state, Nigeria, a mixed methods study. Heliyon. 2021;7:e05977. Available:https://doi.org/10.1016/j.heliyon.2021.e05977
Uzochukwu BSC, Onwujekwe OE, Ezumah N. The district health system in Enugu state, Nigeria: an analysis of policy development and implementation. African Journal of Health Economics. 2014;3:1-14
Ogundokun RO, Lukman AF, Kibria GBM, Awotunde JB, Aladeitan BB. Predictive modelling of COVID-19 confirmed cases in Nigeria. Infectious Disease Modelling. 2020;5:543-548. Available:https://doi.org/10.1016/j.idm.2020.08.003
Adewole MO, Onifade AA, Abdullah FA, Kasali F, Ismail AIM. Modeling the Dynamics of COVID-19 in Nigeria. Int. J. Appl. Comput. Math. 2021;7:67. Available:https://doi.org/10.1007/s40819-021-01014-5
Dlamini WM, Dlamini SN, Mabaso SD, Simelane SP. Spatial risk assessment of an emerging pandemic under data scarcity: A case of COVID-19 in Eswatini. Applied Geography. 2020;125:102358. Available:https://doi.org/10.1016/j.apgeog.2020.102358
Sarkar SK, Ekram KMM, Das PC. Spatial modeling of COVID-19 transmission in Bangladesh. Spat. Inf. Res; 2021. Available:https://doi.org/10.1007/s41324-021-00387-5