1,204
Views
0
CrossRef citations to date
0
Altmetric
Research Article

An approach for measuring spatial similarity among COVID-19 epicenters

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 496-513 | Received 17 Sep 2021, Accepted 07 Jun 2022, Published online: 05 Jul 2022

References

  • Abello, J. M., P. M. Pardalos, and M. G. Resende. 2002. Handbook of Massive Data Sets. NY: Springer.
  • Ahasan, R., M. S. Alam, T. Chakraborty, and M. M. Hossain. 2020. “Applications of GIS and Geospatial Analyses in COVID-19 Research: A Systematic Review.” F1000Research 9: 1379. doi:10.12688/f1000research.27544.1.
  • Ahasan, R., and M. M. Hossain. 2021. “Leveraging GIS and Spatial Analysis for Informed Decision-Making in COVID-19 Pandemic.” Health Policy and Technology 10 (1): 7–9. doi:10.1016/j.hlpt.2020.11.009.
  • Akova, M. 2021. “COVID-19 Vaccination in the Wake of a Fourth Wave of the Pandemic: An Evidence-Based Strategy is Desperately Needed.” Infectious Diseases and Clinical Microbiology 2: 52–54. doi:10.36519/idcm.2021.82.
  • Belitski, M., C. Guenther, A. S. Kritikos, and R. Thurik. 2022. “Economic Effects of the COVID-19 Pandemic on Entrepreneurship and Small Businesses.” Small Business Economics 58: 593–609. doi:10.1007/s11187-021-00544-y.
  • Ben Ishak, A. 2016. “Variable Selection Using Support Vector Regression and Random Forests: A Comparative Study.” Intelligent Data Analysis 20 (1): 83–104. doi:10.3233/IDA-150795.
  • Bhopal, S. S., and R. Bhopal. 2020. “Sex Differential in COVID-19 Mortality Varies Markedly by Age.” Correspondence 396 (10250): 532–533. doi:10.1016/S0140-6736(20)31748-7.
  • De Maesschalck, R., and D. Jouan-Rimbaud, and D. L. Massart. 2000. “The Mahalanobis Distance.” Chemometrics and Intelligent Laboratory Systems 50 (1): 1–18. doi:10.1016/s0169-7439(99)00047-7.
  • Dhamodharavadhani, S., and R. Rathipriya. 2021. “COVID-19 Mortality Rate Prediction for India Using Statistical Neural Networks and Gaussian Process Regression Model.” African Health Sciences 21 (1): 194–206. doi:10.4314/ahs.v21i1.26.
  • Dharani, N. P., P. Bojja, and P. R. Kumari. 2021. “Evaluation of Performance of an LR and SVR Models to Predict COVID-19 Pandemic.” In Materials Today: Proceedings. doi:10.1016/j.matpr.2021.02.166.
  • Díaz-Olalla, J.M., G. Blasco-Novalbos, and I. Valero-Otero. 2021. ”COVID-19 Incidence in Districts of Madrid and Its Relationship with Socio-Economic and Demographic Indicators.” Revista Espanola de Salud Publica 95: e202107091. Spanish. PMID: 34212940.
  • Ding, H. 2004. “A study on Spatial Similarity Theory and Calculation Model.” PhD diss., Wuhan University.
  • Dobesova, Z. 2019. “The Similarity of European Cities Based on Image Analysis.” Intelligent Systems Applications in Software Engineering 341–348. doi:10.1007/978-3-030-30329-7_31.
  • Dzisi, E. K. J., and O. A. Dei. 2020. “Adherence to Social Distancing and Wearing of Masks Within Public Transportation During the COVID 19 Pandemic.” Transportation Research Interdisciplinary Perspectives 7: 100191. doi:10.1016/j.trip.2020.100191.
  • Ehlert, A. 2020. “The Socio-Economic Determinants of COVID-19: A Spatial Analysis of German County Level Data.” Socio-Economic Planning Sciences 78: 101083. doi:10.1016/j.seps.2021.101083.
  • Fan, G. F., L. L. Peng, W. -C. Hong, and F. Sun. 2016. “Electric Load Forecasting by the SVR Model with Differential Empirical Mode Decomposition and Auto Regression.” Neurocomputing 173 (3): 958–970. doi:10.1016/j.neucom.2015.08.051.
  • Franch-Pardo, I., B. M. Napoletano, F. Rosete-Verges, and L. Billa. 2020. “Spatial Analysis and GIS in the Study of COVID-19. A Review.” The Science of the Total Environment 739: 140033. doi:10.1016/j.scitotenv.2020.140033.
  • Franch‐pardo, I., and M. R. Desjardins, I. Barea‐navarro, and A. Cerdà. 2021. “A Review of GIS Methodologies to Analyze the Dynamics of COVID‐19 in the Second Half of 2020.” Transactions in GIS 25 (5): 2191–2239. doi:10.1111/tgis.12792.
  • Furtado, A. S., and D. Kopanaki, L. O. Alvares, and V. Bogorny. 2016. “Multidimensional Similarity Measuring for Semantic Trajectories.” Transactions in GIS 20 (2): 280–298. doi:10.1111/tgis.12156.
  • Gelius, P., A. Tcymbal, S. Whiting, S. Messing, K. Abu-Omar, W. Geidl, A. K. Reimers, et al. 2021. “Impact of the First Wave of COVID-19 on Physical Activity Promotion in the European Union: Results from a Policymaker Survey.” Journal of Physical Activity & Health 18 (12): 1490–1494. doi:10.1123/jpah.2021-0083.
  • Gevrey, M., I. Dimopoulos, and S. Lek. 2003. “Review and Comparison of Methods to Study the Contribution of Variables in Artificial Neural Network Models.” Ecological Modelling 160: 249–264. doi:10.1016/S0304-3800(02)00257-0.
  • Gregorutti, B., B. Michel, and P. Saint-Pierre. 2015. “Grouped Variable Importance with Random Forests and Application to Multiple Functional Data Analysis.” Computational Statistics & Data Analysis 90 (C): 15–35. doi:10.1016/j.csda.2015.04.002.
  • Griffith, D., and B. Li. 2021. “Spatial-Temporal Modeling of Initial COVID-19 Diffusion: The Cases of the Chinese Mainland and Conterminous United States.” Geo-Spatial Information Science 24 (3): 340–362. doi:10.1080/10095020.2021.1937338.
  • Guan, W., Z. Ni, Y. Hu, W. Liang, C. Ou, J. He, L. Liu, et al. 2020. “Clinical Characteristics of Coronavirus Disease 2019 in China.” The New England Journal of Medicine 382: 1708–1720. doi:10.1056/NEJMoa2002032.
  • Guyon, I., A. Elisseeff, and L. P. Kaelbling. 2003. “An Introduction to Variable and Feature Selection.” Journal of Machine Learning Research 3: 1157–1182. doi:10.1162/153244303322753616.
  • Hasani, S., A. Sadeghi-Niaraki, and M. Jelokhani-Niaraki. 2015. “Spatial Data Integration Using Ontology-Based Approach.” The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40 (1): 293‏. doi:10.5194/ISPRSARCHIVES-XL-1-W5-293-2015.
  • Jelokhani-Niaraki, M., and J. Malczewski. 2012. “A User-Centered Multicriteria Spatial Decision Analysis Model for Participatory Decision Making: An Ontology-Based Approach.” In Proceedings of GSDI, Quebec City, 13‏.
  • Jelokhani-Niaraki, M., F. Hajiloo, and N. N. Samany. 2019. “A Web-Based Public Participation GIS for Assessing the Age-Friendliness of Cities: A Case Study in Tehran, Iran.” Cities 95: 102471‏. doi:10.1016/j.cities.2019.102471.
  • Jelokhani-Niaraki, M. 2021. “Collaborative Spatial Multicriteria Evaluation: A Review and Directions for Future Research.” International Journal of Geographical Information Science 35 (1): 9–42. doi:10.1080/13658816.2020.1776870.
  • Jiang, B., and C. de Rijke. 2021. “A Power-Law-Based Approach to Mapping COVID-19 Cases in the United States.” Geo-Spatial Information Science 24 (3): 333–339. doi:10.1080/10095020.2020.1871306.
  • Kaffash Charandabi, N., and A. Gholami. 2021. ”COVID-19 Spatiotemporal Hotspots and Prediction Based on Wavelet and Neural Network. COVID-19 Pandemic.” In Geospatial Information, and Community Resilience Global Applications and Lessons. Boca Raton: CRC Press. doi:10.1201/9781003181590.
  • Kaffash Charandabi, N., and A. Gholami, and A. Abdollahzadeh Bina. 2022. “Road Accident Risk Prediction Using Generalized Regression Neural Network Optimized with Self-Organizing Map.” Neural Computing & Applications 34: 8511–8524. doi:10.1007/s00521-021-06549-8.
  • Kim, J.H., J.A. An, P.K. Min, A. Bitton, and A.A. Gawande. 2020. “How South Korea Responded to the COVID-19 Outbreak in Daegu.” NEJM Catalyst Innovations in Care Delivery.
  • Lai, C. C., and T. P. Shih, W. -C. Ko, H. J. Tang, and P. R. Hsueh. 2020. “Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Coronavirus Disease-2019 (COVID-19): The Epidemic and the Challenges.” International Journal of Antimicrobial Agents 55 (3): 105924. doi:10.1016/j.ijantimicag.2020.105924.
  • Lehmann, A. L., L. O. Alvares, and V. Bogorny. 2019. “SMSM: A Similarity Measure for Trajectory Stops and Moves.” International Journal of Geographical Information Science 1–26. doi:10.1080/13658816.2019.1605074.
  • Li, H., T. Tamang, and C. Nantasenamat. 2021. “Toward Insights on Antimicrobial Selectivity of Host Defense Peptides via Machine Learning Model Interpretation.” Genomics 113 (6): 3851–3863. doi:10.1016/j.ygeno.2021.08.023.
  • Liao, W., D. Hou, and W. Jiang. 2019. “An Approach for a Spatial Data Attribute Similarity Measure Based on Granular Computing Closeness.” Applied Sciences 9 (13): 2628. doi:10.3390/app9132628.
  • Lin, Y., P. Zhong, and T. Chen. 2020. “Association Between Socioeconomic Factors and the COVID-19 Outbreak in the 39 Well-Developed Cities of China.” Front Public Health 8: 546637. https://doi.org/10.3389/fpubh.2020.546637
  • Ma, Y., Y. Zhao, J. Liu, X. He, B. Wang, S. Fu, J. Yan, J. Niu, J. Zhou, and B. Luo. 2020. “Effects of Temperature Variation and Humidity on the Death of COVID-19 in Wuhan, China.” The Science of the Total Environment 724: 138226. doi:10.1016/j.scitotenv.2020.138226.
  • Mishra, S. V., A. Gayen, and S. M. Haque. 2020. “COVID-19 and Urban Vulnerability in India.” Habitat International 103: 102230. doi:10.1016/j.habitatint.2020.102230.
  • Mitchell, A. 2005. The ESRI Guide to GIS Analysis Volume 2: Spatial Measurements and Statistics. Redlands, CA: Esri Press.
  • Mollalo, A., B. Vahedi, and K. M. Rivera. 2020. “GIS-Based Spatial Modeling of COVID-19 Incidence Rate in the Continental United States.” The Science of the Total Environment 728. doi:10.1016/j.scitotenv.2020.138884.
  • Núñez, A., S. D. Sreeganga, and A. Ramaprasad. 2021. ““Access to Healthcare During COVID-19”.” International Journal of Environmental Research and Public Health 18 (6): 2980. doi:10.3390/ijerph18062980.
  • Olden, J. D., M. K. Joy, and R. G. Death. 2004. “An Accurate Comparison of Methods for Quantifying Variable Importance in Artificial Neural Networks Using Simulated Data.” Ecological Modelling 178 (3–4): 389–397. doi:10.1016/j.ecolmodel.2004.03.013.
  • Patra, B. K., S. Nandi, and P. Viswanath. 2011. “A Distance Based Clustering Method for Arbitrary Shaped Clusters in Large Datasets.” Pattern Recognition 44 (12): 2862–2870. doi:10.1016/j.patcog.2011.04.027.
  • Preotiuc-Pietro, D., J. Cranshaw, and T. Yano. 2013. “Exploring Venue-Based City-To-City Similarity Measures.” UrbComp 13: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing 16: 1–4. doi:10.1145/2505821.2505832.
  • Rendana, M. 2020. “Impact of the Wind Conditions on COVID-19 Pandemic: A New Insight for Direction of the Spread of the Virus.” Urban Climate 34: 100680. doi:10.1016/j.uclim.2020.100680.
  • Sannigrahi, S., F. Pilla, B. Basu, A. S. Basu, and A. Molter. 2020. “Examining the Association Between Socio-Demographic Composition and COVID-19 Fatalities in the European Region Using Spatial Regression Approach.” Sustainable Cities and Society 62: 102418. doi:10.1016/j.scs.2020.102418.
  • Setti, L., F. Passarini, G. De Gennaro, P. Barbieri, M.G. Perrone, M. Borelli, J. Palmisani, et al.$3$2 2020. “SARS-Cov-2RNA Found on Particulate Matter of Bergamo in Northern Italy: First Evidence.” Environmental Research 188 (109754). doi:10.1016/j.envres.2020.109754.
  • Sun, C., and Z. Zhai. 2020. “The Efficacy of Social Distance and Ventilation Effectiveness in Preventing COVID-19 Transmission.” Sustainable Cities and Society 62: 102390. doi:10.1016/j.scs.2020.102390.
  • Sung, A. H. 1998. “Ranking Importance of Input Parameters of Neural Networks.” Expert Systems with Applications 15 (3–4): 405–411. doi:10.1016/S0957-4174(98)00041-4.
  • Taghizadeh-Hesary, F., and H. Akbari. 2020. “The Powerful Immune System Against Powerful COVID-19: A Hypothesis.” Medical Hypotheses 140: 109762. doi:10.1016/j.mehy.2020.109762.
  • Termeh, V. R., and A. S. Niaraki. 2015. “Design and Implementation of Ubiquitous Health System (U-Health) Using Smart-Watch Sensors.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences- ISPRS Archives 40 (1W5): 607–612. doi:10.5194/isprsarchives-XL-1-W5-607-2015.
  • UNESCO. 2020. “Monitoring World Heritage Site Closures.” Accessed 14 June 2020. https://en.unesco.org/covid19/cultureresponse/monitoring-worldheritage-site-closures
  • UNHCR (United Nations High Commissioner for Refugees). 2020. “Agd Considerations – Covid-19.” https://data2.unhcr.org/en/documents/download/75295
  • Wan, Y., C. Zhou, and T. Pei. 2017. “Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement.” ISPRS International Journal of Geo-Information 6 (7): 212. doi:10.3390/ijgi6070212.
  • Wang, J., K. Tang, K. Feng, X. Lin, L. Weifeng, K. Chen, and F. Wang. 2020. ”High Temperature and High Humidity Reduce the Transmission of COVID-19.” BMJ Open, arXiv:2003.05003. https://ssrn.com/abstract=3551767_2020
  • Wehenkel, C. 2020. “Positive Association Between COVID-19 Deaths and Influenza Vaccination Rates in Elderly People Worldwide.” PeerJ 8: e10112. doi:10.7717/peerj.10112.
  • Whittle, R.S., and A. Diaz-Artiles. 2020. “An Ecological Study of Socioeconomic Predictors in Detection of COVID-19 Cases Across Neighborhoods in New York City.” BMC Medicine 18: 271. doi:10.1186/s12916-020-01731-6.
  • WHO (World Health Organization). 2021. “WHO Coronavirus (COVID-19) Dashboard.” Accessed 8 December 2021. https://covid19.who.int/
  • WHO (World Health Organization). 2022. “WHO Coronavirus (COVID-19) Dashboard.” WHO. Accessed 11 March 2022. https://covid19.who.int/
  • Wieler, L., U. Rexroth, and R. Gottschalk. 2020. “Emerging Covid-19 Success Story Germany’s Strong Enabling Environment.” Exemplars in Global Health (EGH) platform. https://ourworldindata.org/covid-exemplar-germany
  • Worldometers. “Coronavirus Update (Live).” Accessed 11 March 2022. https://www.worldometers.info/coronavirus/
  • Wu, X., R. C. Nethery, B. M. Sabath, D. Braun, and F. Dominici. 2020. “Exposure to Air Pollution and COVID-19 Mortality in the United States.” Science Advances 6 (45): eabd4049. doi:10.1126/sciadv.abd4049.
  • Wu, Y., W. Jing, J. Liu, Q. Ma, J. Yuan, Y. Wang, M. Du, and M. Liu. 2020. “Effects of Temperature and Humidity on the Daily New Cases and New Deaths of COVID-19 in 166 Countries.” The Science of the Total Environment 729: 139051. doi:10.1016/j.scitotenv.2020.139051.
  • Xiong, Y., B. Mi, A. C. Panayi, L. Chen, and G. Liu. 2020. ”Wuhan: The First Post‐covid‐19 Success Story.” The British Journal of Surgery 107 (10): e431–e431. doi:10.1002/bjs.11875.
  • Xu, T. L., M. Y. Ao, X. Zh, W. F. Zh, H. Y. Nie, J. H. Fang, X. Sun, B. Zheng, and X. F. Chen. 2020. “China’s Practice to Prevent and Control COVID-19 in the Context of Large Population Movement.” Infectious Diseases of Poverty 9 (1): 115. doi:10.1186/s40249-020-00716-0.
  • Yahya, B. M., F. S. Yahya, and R. G. Thannoun. 2021. “COVID-19 Prediction Analysis Using Artificial Intelligence Procedures and GIS Spatial Analyst: A Case Study for Iraq.” Applied Geomatics 13 (3): 481–491. doi:10.1007/s12518-021-00365-4.
  • Yang, M., P. He, X. Xu, D. Li, J. Wang, Y. Wang, B. Wang, W. Wang, M. Zhao, and H. Lin. 2021. “Disrupted Rhythms of Life, Work and Entertainment and Their Associations with Psychological Impacts Under the Stress of the COVID-19 Pandemic: A Survey in 5854 Chinese People with Different Sociodemographic Backgrounds.” Plos One 16 (5): e0250770. doi:10.1371/journal.pone.0250770.
  • Yi, B. K., H. Jagadish, and C. Faloutsos. 1998. “Efficient Retrieval of Similar Time Sequences Under Time Warping.” In Proceedings 14th International Conference on Data Engineering: IEEE, 201–208 ,Orlando, FL, USA.
  • Zadeh, L. A. 1965. “Fuzzy Sets.” Information and Control 8 (3): 338–353. doi:10.1016/S0019-9958(65)90241-X.
  • Zhang, Y., S. Na, J. Niu, and B. Jiang. 2018. “The Influencing Factors, Regional Difference and Temporal Variation of Industrial Technology Innovation: Evidence with the FOA-GRNN Model.” Sustainability 10 (2): 187. doi:10.3390/su10010187.
  • Zhang, Z., T. Xue, and X. Jin. 2020. “Effects of Meteorological Conditions and Air Pollution on COVID-19 Transmission: Evidence from 219 Chinese Cities.” The Science of the Total Environment 741: 140244. doi:10.1016/j.scitotenv.2020.140244.
  • Zhang, J., and X. Yuan. 2021. “COVID-19 Risk Assessment: Contributing to Maintaining Urban Public Health Security and Achieving Sustainable Urban Development.” Sustainability 13 (8): 4208. doi:10.3390/su13084208.
  • Zhao, S., Z. Zhuang, J. Ran, J. Lin, G. Yang, L. Yang, and D. He. 2020. “The Association Between Domestic Train Transportation and Novel Coronavirus (2019-nCov) Outbreak in China from 2019 to 2020: A Data-Driven Correlational Report.” Travel Medicine and Infectious Disease 33: 101568. doi:10.1016/j.tmaid.2020.101568.
  • Zhou, C., F. Su, T. Pei, A. Zhan, Y. Du, B. Luo, and Z. Cao, J. Wang, W. Yuan, Y. Zhu, C. Song, J. Chen, J. Xu, F. Li, T. Ma, L. Jiang, F. Yan, J. Yi, Y. Hu, Y. Liao, and H. Xiao. 2020. “COVID-19: Challenges to GIS with Big Data.” Geography and Sustainability 1 (1): 77–87. doi:10.1016/j.geosus.2020.03.005.
  • Zhou, F., T. Yu, and R. Du, G. Fan, Y. Liu, Z. Liu, J. Xiang, et al. 2020. ”Clinical Course and Risk Factors for Mortality of Adult Inpatients with COVID-19 in Wuhan, China: A Retrospective Cohort Study.” The Lancet 395 (10229): 1054–1062. doi:10.1016/s0140-6736(20)30566-3.
  • Ziani, S. 2017. “Time-Varying Fuzzy Sets Based on Gaussian Membership Functions for Developing Fuzzy Controller.” Iranian Journal of Fuzzy Systems 14 (3): 15–39.