Published September 11, 2023 | Version v1
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Situational-linguistic model of Covid-19 as a tool for ensuring the prevention and management of the pandemic

  • 1. Tashkent Medical Academy Termiz branch, Uzbekistan
  • 2. Department of Informatics, Kharkiv National University of Radio Electronics, Ukraine
  • 3. Department of Media Systems and Technology, Kharkiv National University of Radio Electronics, Ukraine

Description

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

  • 1. Ciotti, M., & et al.. (2020). The COVID-19 pandemic. Critical reviews in clinical laboratory sciences, 57(6), 365-388.
  • 2. Pranggono, B., & Arabo, A. (2021). COVID‐19 pandemic cybersecurity issues. Internet Technology Letters, 4(2), e247.
  • 3. Padhan, R., & Prabheesh, K. P. (2021). The economics of COVID-19 pandemic: A survey. Economic analysis and policy, 70, 220-237.
  • 4. 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.
  • 5. Kaxiras, E., & Neofotistos, G. (2020). Multiple epidemic wave model of the COVID-19 pandemic: modeling study. Journal of medical Internet research, 22(7), e20912.
  • 6. 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.
  • 7. 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.
  • 8. Mustafa, S. K., & et al.. (2021). Some aspects of modeling in the study of COVID-19 data. International Journal of Pharmaceutical Research, 4124-4129.
  • 9. 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.
  • 10. 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.