IDENTIFICATION OF AREAS OF CORONAVIRUS COVID-19 INCIDENCE SPREADING BASED ON CLUSTER ANALYSIS METHOD

Keywords: cluster analysis, neural network, machine learning, epidemic process, COVID-19

Abstract

Subject: the use of the mathematical apparatus of neural networks for the scientific substantiation of anti-epidemic measures in order to reduce the incidence of diseases when making effective management decisions. Purpose: to apply cluster analysis, based on a neural network, to solve the problem of identifying areas of incidence. Tasks: to analyze methods of data analysis to solve the clustering problem; to develop a neural network method for clustering the territory of Ukraine according to the nature of the epidemic process COVID-19; on the basis of the developed method, to implement a data analysis software product to identify the areas of incidence of the disease using the example of the coronavirus COVID-19. Methods: models and methods of data analysis, models and methods of systems theory (based on the information approach), machine learning methods, in particular the Adaptive Boosting method (based on the gradient descent method), methods for training neural networks. Results: we used the data of the Center for Public Health of the Ministry of Health of Ukraine distributed over the regions of Ukraine on the incidence of COVID-19, the number of laboratory examined persons, the number of laboratory tests performed by PCR and ELISA methods, the number of laboratory tests of IgA, IgM, IgG; the model used data from March 2020 to December 2020, the modeling did not take into account data from the temporarily occupied territories of Ukraine; for cluster analysis, a neural network of 60 input neurons, 100 hidden neurons with an activation Fermi function and 4 output neurons was built; for the software implementation of the model, the programming language Python was used. Conclusions: analysis of methods for constructing neural networks; analysis of training methods for neural networks, including the use of the gradient descent method for the Adaptive Boosting method; all theoretical information described in this work was used to implement a software product for processing test data for COVID-19 in Ukraine; the division of the regions of Ukraine into zones of infection with the COVID-19 virus was carried out and a map of this division was presented.

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Author Biographies

Kseniia Bazilevych, National Aerospace University "Kharkiv Aviation Institute"
PhD (Information Technologies), Associate Professor at the Department of Mathematical Modeling and Artificial Intelligence
Ievgen Meniailov, National Aerospace University "Kharkiv Aviation Institute"
Senior Lecturer at the Department of Mathematical Modeling and Artificial Intelligence
Dmytro Chumachenko, National Aerospace University "Kharkiv Aviation Institute"
PhD (Systems and Means of Artificial Intelligence), Associate Professor, Associate Professor at the Department of Mathematical Modeling and Artificial Intelligence

References

Tabik, S., Gomez-Rios, A., Martin-Rodriguez, J., Sevillano-Garcia, I., Rey-Area, M., Charte, D., Guirado, E., Suarez, J., Luengo, J., Valero-Gonzalez, M., Garcia-Villanova, P., Olmedo-Sanchez, E. and Herrera, F. (2020), "COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images", IEEE Journal of Biomedical and Health Informatics, No. 24 (12), P. 3595–3605. DOI: https://doi.org/10.1109/JBHI.2020.3037127

Marmarelis, V. (2020), "Predictive Modeling of Covid-19 Data in the US: Adaptive Phase-Space Approach", IEEE Open Journal of Engineering in Medicine and Biology, P. 207–213. DOI: https://doi.org/10.1109/OJEMB.2020.3008313

Cihan, P. (2020), "Fuzzy Rule-Based System for Predicting Daily Case in COVID-19 Outbreak", P. 1–4. DOI: https://doi.org/10.1109/ISMSIT50672.2020.9254714

Barman, M. and Mishra, N. (2020), "A time-delay SEAIR model for COVID-19 spread", P. 1–6. DOI: https://doi.org/10.1109/CICT51604.2020.9312111

Kapetanović, A. L. and Poljak, D. (2020), "Modeling the Epidemic Outbreak and Dynamics of COVID-19 in Croatia", 5th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, P. 1–5, DOI: https://doi.org/10.23919/SpliTech49282.2020.9243757

Horry, M., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., Shukla, N. (2020), "COVID-19 Detection through Transfer Learning using Multimodal Imaging Data", IEEE Access, Vol. 8, P. 149808–149824. DOI: https://doi.org/10.1109/ACCESS.2020.3016780

Johns Hopkins University & Medicine, "Coronavirus resource center Baltimore", USA : available at : https://coronavirus.jhu.edu/map.html (last accessed 17.02.2021).

Bazilevych, K., Mazorchuk, M., Parfeniuk, Y., Dobriak, V., Meniailov, I., Chumachenko, D. (2018), "Stochastic modelling of cash flow for personal insurance fund using the cloud data storage", International Journal of Computing, Vol. 17, P. 153–162. DOI: https://doi.org/10.47839/ijc.17.3.1035

Bazilevych, K., Meniailov, I., Goranina, S., Fedulov, K. (2019), "Determination of the likelihood of heart disease based on Data Mining methods" ["Opredeleny`e veroyatnosty` zabolevany`ya boleznyamy` serdcza na osnove metodov Data Mining"], Integrated technologies in design and construction, Vol. 83, P. 202–214.

Bazilevych, K., Meniailov, I., Fedulov, K., Goranina, S., Chumachenko, D., Pyrohov P. (2019), "Determining the Probability of Heart Disease using Data Mining Methods", CEUR Workshop Proceedings, Vol. 2488, P. 1–12.

Fang, W. and Lacher, R. C. (1994), "Network complexity and learning efficiency of constructive learning algorithms," Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), Orlando, FL, USA, Vol. 1, P. 366–369. DOI: https://doi.org/10.1109/ICNN.1994.374191

Tuma, A., Haasis, H. and Rentz, O. (1993), "Emission oriented production control strategies based on fuzzy expert systems, neural networks and neuro-fuzzy approaches", Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), Nagoya, Japan, Vol. 3, P. 2971–2974. DOI: https://doi.org/10.1109/IJCNN.1993.714346

Bu, Z., Zhou, B., Cheng, P., Zhang, K., Ling, Z. -H. (2020), "Encrypted Network Traffic Classification Using Deep and Parallel Network-in-Network Models", IEEE Access, Vol. 8, P. 132950–132959. DOI: https://doi.org/10.1109/ACCESS.2020.3010637

Goudreau, M. W., Giles, C. L., Chakradhar, S. T., Chen, D. (1994), "First-order versus second-order single-layer recurrent neural networks", IEEE Trans Neural Netw, No. 5(3), P. 511–3. DOI: https://doi.org/10.1109/72.286928

Guha, D. R. and Patra, S. K. (2010), "Cochannel Interference Minimization Using Wilcoxon Multilayer Perceptron Neural Network", 2010 International Conference on Recent Trends in Information, Telecommunication and Computing, Kerala, India, P. 145–149. DOI: https://doi.org/10.1109/ITC.2010.50

Hirahara, M. and Oka, N. (1993), "A hybrid model composed of a multilayer perceptron and a radial basis function network", Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), Nagoya, Japan, P. 1353–1356, Vol. 2. DOI: https://doi.org/10.1109/IJCNN.1993.716794

Postarnak, D. V. (2012), "Critical analysis of neural network models" ["Kry`ty`chesky`j analy`z modelej nejronnыx setej"], Bulletin of the Tyumen State University, No. 4, P. 162–167.

"Analytical panels and open data: Official channel of the Ministry of Health of Ukraine", available at : https://covid19.gov.ua/analitichni-paneli-dashbordy (last accessed 17.02.2021).

Yakovlev, S., Bazilevych, K., Chumachenko, D., Chumachenko, T., Hulianytskyi, L., Meniailov, I., Tkachenko, A. (2020), "The Concept of Developing a Decision Support System for the Epidemic Morbidity Control", CEUR Workshop Proceedings, Vol. 2753, P. 265–274.


Abstract views: 22
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Published
2021-03-28
How to Cite
Bazilevych, K., Meniailov, I. and Chumachenko, D. (2021) “IDENTIFICATION OF AREAS OF CORONAVIRUS COVID-19 INCIDENCE SPREADING BASED ON CLUSTER ANALYSIS METHOD”, INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, (1 (15), pp. 5-13. doi: 10.30837/ITSSI.2021.15.005.