Vol. 1 No. 9 (2023), Articles
Vol. 1 No. 9 (2023)

Situational-linguistic model of Covid-19 as a tool for ensuring the prevention and management of the pandemic

Articles
Published 2023-09-10
Boboyorov Sardor Uchqun o‘g‘li
Tashkent Medical Academy Termiz branch, Uzbekistan
Oleksandr Kuzomin
Department of Informatics, Kharkiv National University of Radio Electronics, Ukraine
Lyashenko Vyacheslav
Department of Media Systems and Technology, Kharkiv National University of Radio Electronics, Ukraine
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Keywords

Model, Covid-19, Prevention, Management, Pandemic

How to Cite

Situational-linguistic model of Covid-19 as a tool for ensuring the prevention and management of the pandemic. (2023). Journal of Universal Science Research, 1(9), 111-121. https://universalpublishings.com/index.php/jusr/article/view/1898
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Abstract

Modeling is one of the tools for cognition and study of phenomena, processes, and objects. This approach allows you to assess the current situation and make the most effective decisions. It also becomes possible to evaluate possible solutions without negatively impacting the current situation. Based on this, the work examines the key aspects of constructing a situational linguistic model in the context of the development of a pandemic. The Covid-19 pandemic is considered such a pandemic. A generalized concept of the situational-linguistic model of Covid-19 is presented.

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References

Ciotti, M., & et al.. (2020). The COVID-19 pandemic. Critical reviews in clinical laboratory sciences, 57(6), 365-388.

Pranggono, B., & Arabo, A. (2021). COVID‐19 pandemic cybersecurity issues. Internet Technology Letters, 4(2), e247.

Padhan, R., & Prabheesh, K. P. (2021). The economics of COVID-19 pandemic: A survey. Economic analysis and policy, 70, 220-237.

Adiga, A., & et al.. (2020). Mathematical models for covid-19 pandemic: a comparative analysis. Journal of the Indian Institute of Science, 100(4), 793-807.

Kaxiras, E., & Neofotistos, G. (2020). Multiple epidemic wave model of the COVID-19 pandemic: modeling study. Journal of medical Internet research, 22(7), e20912.

Mustafa, S. K., & et al.. (2020). Using wavelet analysis to assess the impact of COVID-19 on changes in the price of basic energy resources. International Journal of Emerging Trends in Engineering Research, 8(7), 2907-2912.

Mustafa, S. K., & et al.. (2020). Brief review of the mathematical models for analyzing and forecasting transmission of COVID-19. Journal of critical reviews, 7(19), 4206-4210.

Mustafa, S. K., & et al.. (2021). Some aspects of modeling in the study of COVID-19 data. International Journal of Pharmaceutical Research, 4124-4129.

Babker, A. M. A., & et al.. (2020). COVID-19 data based on wavelet coherence estimates for selected countries in the Eastern Mediterranean. World Journal of Advanced Research and Reviews, 6(3), 110-120.

Orobinskyi, P., Petrenko, D., & Lyashenko, V. (2019, February). Novel Approach to Computer-Aided Detection of Lung Nodules of Difficult Location with Use of Multifactorial Models and Deep Neural Networks. In 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM) (pp. 1-5). IEEE.

Tvoroshenko, I., & et al.. (2020). Modification of models intensive development ontologies by fuzzy logic. International Journal of Emerging Trends in Engineering Research, 8(3), 939-944.

Kuzemin, O., & Lyashenko, V. Microsituation Concept in GMES Decision Support Systems / A. Kuzemin, V. Lуashenko. Intelligent Data Processing in Global Monitoring for Environment and Security (pр. 217–238).–2011.–Р, 217-238.

Baranova, V., & et al.. (2019, October). Stochastic Frontier Analysis and Wavelet Ideology in the Study of Emergence of Threats in the Financial Markets. In 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T) (pp. 341-344). IEEE.

Lyashenko, V., & et al.. (2021). Modern Walking Robots: A Brief Overview. International Journal of Recent Technology and Applied Science, 3(2), 32-39.

Jewell, N. P., Lewnard, J. A., & Jewell, B. L. (2020). Predictive mathematical models of the COVID-19 pandemic: underlying principles and value of projections. Jama, 323(19), 1893-1894.

Khajanchi, S., & et al.. (2021). Mathematical modeling of the COVID-19 pandemic with intervention strategies. Results in Physics, 25, 104285.

Iboi, E., Sharomi, O. O., Ngonghala, C., & Gumel, A. B. (2020). Mathematical modeling and analysis of COVID-19 pandemic in Nigeria. MedRxiv, 2020-05.

Chang, S. L., & et al.. (2020). Modelling transmission and control of the COVID-19 pandemic in Australia. Nature communications, 11(1), 5710.

Noor, M. A., & et al.. (2022). Non-standard computational analysis of the stochastic COVID-19 pandemic model: An application of computational biology. Alexandria Engineering Journal, 61(1), 619-630.

Alguliyev, R., Aliguliyev, R., & Yusifov, F. (2021). Graph modelling for tracking the COVID-19 pandemic spread. Infectious disease modelling, 6, 112-122.

Guan, L., & et al.. (2020). Transport effect of COVID-19 pandemic in France. Annual reviews in control, 50, 394-408.

Malki, Z., & et al.. (2021). The COVID-19 pandemic: prediction study based on machine learning models. Environmental science and pollution research, 28, 40496-40506.

Abbes, A., Ouannas, A., Shawagfeh, N., & Jahanshahi, H. (2023). The fractional-order discrete COVID-19 pandemic model: stability and chaos. Nonlinear Dynamics, 111(1), 965-983.

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