ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 12. Vol. 29. 2023

DOI: 10.17587/it.29.630-638

A. Yu. Perevaryukha, Ph.D., Senior Researcher,
St. Petersburg Federal Research Center of the Russian Academy of Sciences, Saint Petersburg, Russian Federation

Model of Wave Transformation of a Decaying Trend of the COVID-19 Epidemic

The COVID-19 pandemic in 2023 can be divided according to characteristics into groups of regional epidemics. Local processes demonstrate oscillatory dynamics in the form of special forms of oscillations. COVID waves and peaks cannot be considered classic damped relaxation oscillations. Often, the damping amplitude of infection waves formed after the initial outbreak again turns into an extreme peak. The reasons may be effects after crisis events: mass infections and/or an increase in virulence of a new strain that evades vaccine immunity. Using simulation tools, we analyzed a number of variants of epidemic dynamics with sudden changes. We have identified special important epidemic scenarios for the sudden occurrence of a short wave and computationally investigated on the basis of the proposed nonlinear equations with a deviating argument as the most likely development option for a given parametrically simulated situation. Each COVID outbreak is a positive feedback loop: the more infections, the higher the likelihood of further mutations in the virus and strains evading antibodies. At the end of 2023, after a long summer minimum, the incidence rate resumed due to strains of the new Omicron branch of BA.2.xx "Pirola". Keywords: modeling of oscillating epidemic dynamics, attenuation of infection waves, models with destruction of cyclic trajectories, situational computational experiments, epidemic situation differentiation factors

P. 630-638

References

1. Mikhailov V. V., Trofimova I. V., Perevaryukha A. Yu. Computational Modeling of the Nonlinear Metabolism Rate as a Trigger Mechanism of Extreme Dynamics of Invasion Processes, Technical Physics Letters, 2022, vol. 48, pp. 301—304.
2. Perevaryukha A. Yu. Nonlinear models and optimization features in the problem of system analysis of population dynamics, Information technologies, 2009, no. 1, pp. 77—81 (in Russian).
3. Perevaryukha A. Yu. Formation of a hybrid computational model for the analysis of critical control scenarios in biocybernetics, Information technologies, 2021, vol. 27, no. 7, pp. 380—390 (in Russian).
4. Ioannidis J. P. A., Cripps S., Tanner M. Forecasting for COVID-19 has failed, International Journal of Forecasting, 2022, no. 2, pp. 423—438.
5. Ρ hin V., Samia N., Marchant R. A case study in model failure? COVID-19 daily deaths and ICU bed utilisation predictions in New York state, European Journal of Epidemiology, 2020, vol. 35, pp. 733—742, https://doi.org/10.1007/s10654-020-00669-6
6. Moein S., Nickaeen N., Roointan A. Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan, Scientific Reports, 2021, vol. 11, art no. 4725, https://doi.org/10.1038/s41598-021-84055-6
7. Nikitina A. V. Study of the spread of viral diseases based on modifications of the SIR model, Computational Mathematics and Information Technologies, 2020, no. 1, pp. 19—30.
8. Perevaryukha A. Yu. A Continuous Model of Three Scenarios of the Infection Process with Delayed Immune Response Factors, Biophysics, 2021, vol. 66, pp. 327—348. https://doi.org/10.1134/S0006350921020160
9. Hutchinson G. E. Circular causal systems in ecology, Ann. N. Y. Acad. Sci., 1948, vol. 50, no. 4, pp. 221—246.
10. Kolesov A. Yu., Kolesov Yu. S. Relaxation oscillations in mathematical models of ecology, Proceedings of the Steklov Mathematical Institute, 1993, vol. 199, pp. 3—124 (in Russian).
11. Trofimova I. V., Perevaryukha A. Yu., Manvelova A. B. Adequacy of Interpretation of Monitoring Data on Biophysical Processes in Terms of the Theory of Bifurcations and Chaotic Dynamics, Technical Physics Letters, 2022, vol, 48, pp. 305—310, https://doi.org/10.1134/S1063785022110025
12. Fabiano N., Radenovic S. N. The second COVID-19 wave of 2020 in Italy a brief analysis, Military Technical Courier, 2021, vol. 69, no. 1, pp. 1—7.
13. Shabunin A. V. Hybrid SIRS model of the spread of infections, Applied nonlinear dynamics, 2022, vol. 30, no. 6, pp. 717—731 (in Russian).
14. Abotaleb M. S., Makarovskikh T. A. Development of algorithms for choosing the best time series models and neural networks to predict COVID-19 cases, Bulletin of the South Ural State University, 2021, vol. 21, no. 3, pp. 26—35.
15. Zaikovskaya A. V., Gladysheva A. V., Kartashov M. Yu. Study in vitro of the biological properties of SARS-COV-2 coro-navirus strains related to various genetic variants, Problems of especially dangerous infections, 2022, no. 1, pp. 94—100.
16. Vechorko V. I., Averkov O. V., Zimin A. A. New strain of SARS-COV-2 Omicron clinical picture, treatment, prevention (literature review), Cardiovascular therapy and prevention, 2022, vol. 21, no. 6, pp. 89—98 (in Russian).
17. Silva S. J., Pardee K., Pena L., Kohl A. Recent insights into SARS-COV-2 omicron variant, Reviews in Medical Virology, 2022, vol. 33, art no: e2373.
18. Skripka I. R. The fight against COVID-19 in Norway and Sweden: analysis of approaches and consequences, Scientific and Analytical Bulletin of the Institute of Europe RAS, 2021, no. 1, pp. 118—125.
19. Brusselaers N., Steadson D., Bjorklund K. Evaluation of science advice during the COVID-19 pandemic in Sweden, Humanities and Social Sciences Communications, 2022, vol. 9, art no: 91, doi.org/10.1057/s41599-022-01097-5
20. Phetsouphanh C., Darley D. R., Wilson D. B. Immunological dysfunction persists for 8 months following initial mild-to-moderate SARS-CoV-2 infection, Nature Immunology, 2022, pp. 210—216 https://doi.org/10.1038/s41590-021-01113-x
21. Oshitani H. COVID lessons from Japan: the right messaging empowers citizens, Nature, 2022, vol. 605, art no: 589, https:// doi.org/10.1038/d41586-022-01385-9
22. Corey L., Beyrer C., Cohen M., Michael N., Bedford T., Rolland M. SARS-CoV-2 variants in patients with immunosuppression, New England Journal of Medicine, 2021, vol. 385, pp. 562—566, doi:10.1056/NEJMsb2104756. pmid:34347959
23. Markov P. V., Ghafari M., Beer M. The evolution of SARS-CoV-2, Nature Reviews Microbiology, 2023, vol. 21, pp. 195—210.

To the contents