Neural network modeling of the economic and social development trajectory transformation due to quarantine restrictions during COVID-19

Vasylieva, Tetiana Anatoliivna; Kuzmenko, Olha Vitaliivna; Kuryłowicz, M.; Letunovska, Nataliia Yevhenivna

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Title	Neural network modeling of the economic and social development trajectory transformation due to quarantine restrictions during COVID-19
Authors	Vasylieva, Tetiana Anatoliivna Kuzmenko, Olha Vitaliivna Kuryłowicz, M. Letunovska, Nataliia Yevhenivna
ORCID	http://orcid.org/0000-0003-0635-7978 http://orcid.org/0000-0001-8575-5725 http://orcid.org/0000-0001-8207-9178
Keywords	impact of COVID-19 forecast of quarantine measures impact socio-economic development of Ukraine economic mathematical model neural network
Type	Article
Date of Issue	2021
URI	https://essuir.sumdu.edu.ua/handle/123456789/84595
Publisher	Centre of Sociological Research in co-operation with University of Szczecin (Poland); Széchenyi István University (Hungary); Mykolas Romeris University (Lithuania); Dubcek University of Trencín, Faculty of Social and Economic Relations (Slovak Republic)
License	Creative Commons Attribution 4.0 International License
Citation	Vasilyeva, T., Kuzmenko, O., Kuryłowicz, M., & Letunovska, N. (2021). Neural network modeling of the economic and social development trajectory transformation due to quarantine restrictions during COVID-19. Economics and Sociology, 14(2), 313-330. doi:10.14254/2071-789X.2021/14-2/17
Abstract	The article uses neural networks to model the effects of quarantine restrictions on the most important indicators of the country's socio-economic development. The authors selected the most relevant indicators and formed a specific sequence of its calculation to study the direction of transforming the trajectory of socio-economic development of a particular country due to quarantine restrictions. They used a multilayer MLP perceptron and networks based on radial basis functions. They chose BFGS and RBFT algorithms in neural network modeling. Collinearity study was the basis for data mining in terms of key factors of change. The author's approach is unique due to an iterative procedure of numerical optimization and quasi-Newton methods ("self-learning" and step-by-step "improvement" of neural networks). The model projected gross domestic product and the number of unemployed in the country affected by the COVID-19 pandemic over the three years.
Appears in Collections:	Наукові видання (ННІ БіЕМ)