The Assessment of COVID-19 Vulnerability Risk for Crisis Management
Source abstract
Abstract
The subject of this article is to determine COVID-19 vulnerability risk and its change over time in association with the state health care system, turnover, and transport to support the crisis management decision-making process. The aim was to determine the COVID-19 Vulnerability Index (CVI) based on the selected criteria. The risk assessment was carried out with methodology that includes the application of multicriteria analysis and spatiotemporal aspect of available data. Particularly the Spatial Multicriteria Analysis (SMCA) compliant with the Analytical Hierarchy Process (AHP), which incorporated selected population and environmental criteria were used to analyse the ongoing pandemic situation. The influence of combining several factors in the pandemic situation analysis was illustrated. Furthermore, the static and dynamic factors to COVID-19 vulnerability risk were determined to prevent and control the spread of COVID-19 at the early stage of the pandemic situation. As a result, areas with a certain level of risk in different periods of time were determined. Furthermore, the number of people exposed to COVID-19 vulnerability risk in time was presented. These results can support the decision-making process by showing the area where preventive actions should be considered.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Soil and Land Suitability Analysis · Multi-Criteria Decision Making
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Geospatial Engineering and Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology (WAT), 00-908 Warsaw, Poland
Funding
Military University of Technology in Warsaw
References (93)
Show source-provided references
1. SARS-CoV-2: An Emerging Coronavirus That Causes a Global Threat10.7150/ijbs.45053 ↗
2. Changes in Risk Perception and Protective Behavior during the First Week of the COVID-19 Pandemic in the United States10.1098/rsos.200742 ↗
3. Geo-Spatial Information and Technologies in Support of EU Crisis Management10.1080/17538940903506014 ↗
4. (2021, November 19). Van Eck: VOSviewer Manual: Version 1.6. 5—Google Scholar. Available online: https://scholar.google.com/scholar_lookup?title=VOSviewer+Manual,+Version+1.6.9&author=van+Eck,+N.J.&author=Waltman,+L.&publication_year=2018.
5. Land Suitability Assessment for Locating Industrial Parks: A Hybrid Multi Criteria Decision-Making Approach Using Geographical Information System10.1111/1745-5871.12176 ↗
6. Using GIS and Outranking Multicriteria Analysis for Land-Use Suitability Assessment10.1080/13658810051030487 ↗
7. Suitability Estimation for Urban Development Using Multi-Hazard Assessment Map10.1016/j.scitotenv.2016.10.025 ↗
8. Review—Agricultural Land Suitability Analysis: State-of-the-Art and Outlooks for Integration of Climate Change Analysis10.1016/j.agsy.2019.02.013 ↗
9. A Grid Algorithm Suitable for Line and Area Feature Label Placement10.1007/s12665-016-6190-4 ↗
10. Terrain Evaluation, Military Purposesterrain Evaluation, Military Purposes10.1007/0-387-30842-3_69 ↗
11. Sarnowski, Ł., Podgórski, Z., and Brykała, D. (2016). Planning a Greenway Based on an Evaluation of Visual Landscape Attractiveness, Biblioteka Uniwersytetu Kazimierza Wielkiego.10.1515/mgr-2016-0017 ↗
12. Integrating Multi-Criteria Evaluation with Geographical Information Systems10.1080/02693799108927858 ↗
13. Portfolio Analysis and Geographical Allocation of Renewable Sources: A Stochastic Approach10.1016/j.enpol.2018.10.034 ↗
14. Kresse, W., and Danko, D.M. (2012). GIS for Transportation. Springer Handbook of Geographic Information, Springer. Springer Handbooks.10.1007/978-3-540-72680-7 ↗
15. The Association between Domestic Train Transportation and Novel Coronavirus (2019-NCoV) Outbreak in China from 2019 to 2020: A Data-Driven Correlational Report10.1016/j.tmaid.2020.101568 ↗
16. Infrastructure Assessment Decision Making During a Crisis10.3141/2093-11 ↗
17. An Efficient Ambulance Routing System for Emergency Cases Based on Dijkstra’s Algorithm, AHP, and GIS
18. Multi-Criteria Emergency Route Planning Based on Analytical Hierarchy Process and PgRouting
19. Pelgrum, H. (2000). Spatial Aggregation of Land Surface Characteristics: Impact of Resolution of Remote Sensing Data on Land Surface Modelling, Wageningen University and Research ProQuest Dissertations Publishing.
20. A Model Based on Cellular Automata for Investigating the Impact of Lockdown, Migration and Vaccination on COVID-19 Dynamics10.1016/j.cmpb.2021.106402 ↗
21. Shi, P., Dong, Y., Yan, H., Li, X., Zhao, C., Liu, W., He, M., Tang, S., and Xi, S. (2020). The Impact of Temperature and Absolute Humidity on the Coronavirus Disease 2019 (COVID-19) Outbreak-Evidence from China. MedRxiv.10.1101/2020.03.22.20038919 ↗
22. Using Artificial Neural Networks to Determine the Location of Wind Farms. Miedzna District Case Study10.1515/jwld-2016-0026 ↗
23. Distribution of the COVID-19 Epidemic and Correlation with Population Emigration from Wuhan, China10.1097/CM9.0000000000000782 ↗
24. Communicating Geographical Risks in Crisis Management: The Need for Research10.1111/risa.12904 ↗
25. A Dynamic Decision Support System Based on Geographical Information and Mobile Social Networks: A Model for Tsunami Risk Mitigation in Padang, Indonesia10.1016/j.ssci.2015.09.022 ↗
26. A Spatio-temporal Population Model to Support Risk Assessment and Damage Analysis for Decision-making10.1080/13658810701349078 ↗
27. Forest Fire Risk Maps: A GIS Open Source Application—A Case Study in Norwest of Portugal10.1080/13658816.2012.721554 ↗
28. Geostatistical Methods for Modelling Non-Stationary Patterns in Disease Risk10.1016/j.spasta.2019.100397 ↗
29. Leone, F., Colas, A., Garcin, Y., Eckert, N., Jomelli, V., and Gherardi, M. (2014). The Snow Avalanches Risk on Alpine Roads Network Assessment of Impacts and Mapping of Accessibility Loss. Rev. Geogr. Alp.-J. Alp. Res., 102.10.4000/rga.2501 ↗
30. Multi-Criteria Decision-Making for Flood Risk Management: A Survey of the Current State of the Art10.5194/nhess-16-1019-2016 ↗
31. GIS-Based COVID-19 Vulnerability Mapping in the West Bank, Palestine10.1016/j.ijdrr.2021.102483 ↗
32. GIS-Based Multi-Criteria Modelling of Flood Risk Susceptibility in Port Louis, Mauritius: Towards Resilient Flood Management10.1016/j.ijdrr.2021.102683 ↗
33. Assessing Ecosystem Services and Biodiversity Tradeoffs across Agricultural Landscapes in a Mountain Region10.1080/21513732.2018.1526214 ↗
34. Spatial Multi-Criteria Analysis in Environmental Assessment: A Review and Reflection on Benefits and Limitations10.1142/S146433321840001X ↗
35. Advances in Spatial Risk Analysis10.1111/risa.13260 ↗
36. Majlingova, A., and VelKova, V. (2017, January 3–5). Crisis Management Based on GIS—Case Study. Proceedings of the Fire Protection, Safety and Security 2017, Zvolen, Slovakia.
37. Rahman, A., Isikdag, U., and Castro, F. (2015, January 28–30). Generalized Cartographic and Simultaneous Representation of Utility Networks for Decision-Support Systems and Crisis Management in Urban Environments. Proceedings of the International Geoinformation Conference, Kuala Lumpur, Malaysia.
38. Rapid Response to COVID-19: Health Informatics Support for Outbreak Management in an Academic Health System10.1093/jamia/ocaa037 ↗
39. Loffler, J., and Klann, M. (2007). Intelligent Cartographic Presentations for Emergency Situations, Springer.
40. Zlatanova, S., Dilo, A., Peters, R., and Scholten, H. (2013). Interactive Simulation and Visualisation of Realistic Flooding Scenarios, Springer.
41. Dilo, A., and Zlatanova, S. (2008, January 4). Spatiotemporal Data Modeling for Disaster Management in The Netherlands. Proceedings of the Joint ISCRAM-CHINA and GI4DM Conference, Harbin, China.
42. (2021, November 07). The Use of Subjective-Objective Weights in GIS-Based Multi-Criteria Decision Analysis for Flood Hazard Assessment: A Case Study in Mazandaran, Iran-Web of Science Core Collection. Available online: https://www-1webofscience-1com-100003exr00f6.han.wat.edu.pl/wos/woscc/full-record/WOS:000617914700020.
43. Performance Evaluation of the GIS-Based Data Mining Techniques of Best-First Decision Tree, Random Forest, and Naive Bayes Tree for Landslide Susceptibility Modeling10.1016/j.scitotenv.2018.06.389 ↗
44. Análise espacial das condições de enfrentamento à COVID-19: Uma proposta de Índice da Infraestrutura da Saúde do Brasil10.1590/1413-812320202512.34472020 ↗
45. (2021, December 09). Water Crisis Analysis Using GIS; Case Study: Nishabur Plain, Iran. Available online: https://scialert.net/fulltext/?doi=ajps.2007.884.891.10.3923/ajps.2007.884.891 ↗
46. Budzynski, M., Luczkiewicz, A., and Szmaglinski, J. (2021). Assessing the Risk in Urban Public Transport for Epidemiologic Factors. Energies, 14.10.3390/en14154513 ↗
47. Davidovsky, A. (2020). System Analysis and Forecasting of the Relationship between Economic Factors and the Epidemic Process COVID-19 to Optimize International Transport Communications in the Context of a Pandemic, Atlantis Press.10.2991/assehr.k.201105.084 ↗
48. Landslide Susceptibility Mapping Using Random Forest and Boosted Tree Models in Pyeong-Chang, Korea10.1080/10106049.2017.1323964 ↗
49. Is Multi-Hazard Mapping Effective in Assessing Natural Hazards and Integrated Watershed Management?10.1016/j.gsf.2019.10.008 ↗
50. Spatial Modeling, Risk Mapping, Change Detection, and Outbreak Trend Analysis of Coronavirus (COVID-19) in Iran (Days between February 19 and June 14, 2020)10.1016/j.ijid.2020.06.058 ↗
51. GIS-Multicriteria Decision Analysis for Landslide Susceptibility Mapping: Comparing Three Methods for the Urmia Lake Basin, Iran10.1007/s11069-012-0463-3 ↗
52. Landslide Susceptibility Assesssment in the Uttarakhand Area (India) Using GIS: A Comparison Study of Prediction Capability of naïve Bayes, Multilayer Perceptron Neural Networks, and Functional Trees Methods10.1007/s00704-015-1702-9 ↗
53. Vulnerability Assessment for Flash Floods Using GIS Spatial Modeling and Remotely Sensed Data in El-Arish City, North Sinai, Egypt10.1007/s11069-019-03571-x ↗
54. Jena, R., Pradhan, B., Al-Amri, A., Lee, C.W., and Park, H.-J. (2020). Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning. Sensor, 20.10.3390/s20164369 ↗
55. (2021, November 07). Web-Based Geospatial Multiple Criteria Decision Analysis Using Open Software and Standards-Web of Science Core Collection. Available online: https://www-1webofscience-1com-100003exr00f6.han.wat.edu.pl/wos/woscc/full-record/WOS:000374902300010.
56. A GIS Based Spatially-Explicit Sensitivity and Uncertainty Analysis Approach for Multi-Criteria Decision Analysis10.1016/j.cageo.2013.11.009 ↗
57. Warren, M.S., and Skillman, S.W. (2020). Mobility Changes in Response to COVID-19. arXiv.
58. Taking the Pulse of COVID-19: A Spatiotemporal Perspective10.1080/17538947.2020.1809723 ↗
59. (2021, November 27). Zheng: Spatial Transmission of COVID-19 via Public…—Google Scholar. Available online: https://scholar.google.com/scholar_lookup?title=Spatial+transmission+of+COVID-19+via+public+and+private+transportation+in+China&author=Zheng,+R.&author=Xu,+Y.&author=Wang,+W.&author=Ning,+G.&author=Bi,+Y.&publication_year=2020&journal=Travel+Med.+Infect.+Dis.&volume=34&pages=101626&doi=10.1016/j.tmaid.2020.101626.10.1016/j.tmaid.2020.101626 ↗
60. Effects of Temperature Variation and Humidity on the Death of COVID-19 in Wuhan, China10.1016/j.scitotenv.2020.138226 ↗
61. Shakhovska, N., Izonin, I., and Melnykova, N. (2021). The Hierarchical Classifier for COVID-19 Resistance Evaluation. Data, 6.10.3390/data6010006 ↗
62. Liu, Q., Liu, W., Sha, D., Kumar, S., Chang, E., Arora, V., Lan, H., Li, Y., Wang, Z., and Zhang, Y. (2020). An Environmental Data Collection for COVID-19 Pandemic Research. Data, 5.10.3390/data5030068 ↗
63. Ferreira, M.C. (2020). Spatial Association between the Incidence Rate of COVID-19 and Poverty in the São Paulo Municipality, Brazil. Geospat. Health, 15.10.4081/gh.2020.921 ↗
64. Risk Perceptions of COVID-19 around the World10.1080/13669877.2020.1758193 ↗
65. A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model10.3389/fpubh.2020.00230 ↗
66. (2021, November 27). Chen: Transmission Dynamics of a Two-City SIR Epidemic…—Google Scholar. Available online: https://scholar.google.com/scholar_lookup?title=Transmission+dynamics+of+a+two-city+SIR+epidemic+model+with+transport-related+infections&author=Chen,+Y.&author=Yan,+M.&author=Xiang,+Z.&publication_year=2014&journal=J.+Appl.+Math.&volume=2014&doi=10.1155/2014/764278.10.1155/2014/764278 ↗
67. Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events10.1007/s11269-018-1922-8 ↗
68. (2021, November 19). Epidemic Analysis of COVID-19 in Italy Based on Spatiotemporal Geographic Information and Google Trends-Niu-2021-Transboundary and Emerging Diseases—Wiley Online Library. Available online: https://onlinelibrary.wiley.com/doi/10.1111/tbed.13902.
69. Mościcka, A., Araszkiewicz, A., Wabiński, J., Kuźma, M., and Kiliszek, D. (2021). Modeling of Various Spatial Patterns of SARS-CoV-2: The Case of Germany. J. Clin. Med., 10.10.3390/jcm10071409 ↗
70. Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States10.1093/cid/ciaa934 ↗
71. Evaluation of Multi-Hazard Map Produced Using MaxEnt Machine Learning Technique10.1038/s41598-021-85862-7 ↗
72. Sha, D., Malarvizhi, A.S., Liu, Q., Tian, Y., Zhou, Y., Ruan, S., Dong, R., Carte, K., Lan, H., and Wang, Z. (2020). A State-Level Socioeconomic Data Collection of the United States for COVID-19 Research. Data, 5.10.3390/data5040118 ↗
73. Role of Intelligent Computing in COVID-19 Prognosis: A State-of-the-Art Review10.1016/j.chaos.2020.109947 ↗
74. Zhang, Z., Cheshmehzangi, A., and Ardakani, S.P. (2021). A Data-Driven Clustering Analysis for the Impact of COVID-19 on the Electricity Consumption Pattern of Zhejiang Province, China. Energies, 14.10.3390/en14238187 ↗
75. Nagaj, R., and Žuromskaitė, B. (2021). Tourism in the Era of COVID-19 and Its Impact on the Environment. Energies, 14.10.3390/en14072000 ↗
76. Navon, A., Machlev, R., Carmon, D., Onile, A.E., Belikov, J., and Levron, Y. (2021). Effects of the COVID-19 Pandemic on Energy Systems and Electric Power Grids—A Review of the Challenges Ahead. Energies, 14.10.3390/en14041056 ↗
77. Phytoplankton Bloom Detection during the COVID-19 Lockdown with Remote Sensing Data: Using Copernicus Sentinel-3 for North-Western Arabian/Persian Gulf Case Study10.1016/j.marpolbul.2021.112734 ↗
78. An Impact Study of COVID-19 on Six Different Industries: Automobile, Energy and Power, Agriculture, Education, Travel and Tourism and Consumer Electronics10.1111/exsy.12677 ↗
79. (2021, December 27). Chretien: Influenza Forecasting in Human Populations:…—Google Scholar. Available online: https://scholar.google.com/scholar_lookup?title=Influenza%20forecasting%20in%20human%20populations%3A%20a%20scoping%20review&publication_year=2014&author=J.P.%20Chretien&author=D.%20George&author=J.%20Shaman&author=R.A.%20Chitale&author=F.E.%20McKenzie.
80. COVID-19 Risk Governance: Drivers, Responses and Lessons to Be Learned10.1080/13669877.2020.1760332 ↗
81. (2021, November 19). Board: A World at Risk—Google Scholar. Available online: https://scholar.google.com/scholar_lookup?hl=en&publication_year=2019&author=Global+Preparedness+Monitoring+Board&title=A+World+at+Risk%3A+Annual+Report+on+Global+Preparedness+for+Health+Emergencies.
82. A GIS-Based Decision Support System for Reducing Air Ambulance Response Times: A Case Study on Public Schools in Jeddah City
83. How Should US Hospitals Prepare for Coronavirus Disease 2019 (COVID-19)?10.7326/M20-0907 ↗
84. Preparation for Possible Sustained Transmission of 2019 Novel Coronavirus: Lessons From Previous Epidemics10.1001/jama.2020.1960 ↗
85. The Experiences of Health-Care Providers during the COVID-19 Crisis in China: A Qualitative Study10.1016/S2214-109X(20)30204-7 ↗
86. GIS-based Multicriteria Decision Analysis: A Survey of the Literature10.1080/13658810600661508 ↗
87. Emerging Trends and Research Frontiers in Spatial Multicriteria Analysis10.1080/13658816.2020.1712403 ↗
88. Measuring Community Vulnerability to Natural and Anthropogenic Hazards: The Centers for Disease Control and Prevention’s Social Vulnerability Index
89. Saaty, T.L. (1990). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, RWS.10.1108/eb022672 ↗
90. (2022, February 01). Corona-Pandemie Führt zu Übersterblichkeit in Deutschland. Available online: https://www.destatis.de/DE/Presse/Pressemitteilungen/2021/12/PD21_563_12.html.
91. (2022, February 01). RKI-COVID-19-COVID-19. Available online: https://www.rki.de/EN/Content/infections/epidemiology/outbreaks/COVID-19/COVID19.html.
92. (2022, February 01). OpenStreetMap. Available online: https://www.openstreetmap.org/.
93. (2021, November 25). COVID-19-Trends in Deutschland Im Überblick. Available online: https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/COVID-19-Trends/COVID-19-Trends.html?__blob=publicationFile#/home.