Balance Model of COVID-19 Epidemic Based on Percentage Growth Rate
Keywords:
COVID-19, epidemic modeling of new viruses, SIR models, forecastAbstract
The paper examines the possibility of using an alternative approach to predicting statistical indicators of a new COVID-19 virus type epidemic. A systematic review of models for predicting epidemics of new infections in foreign and Russian literature is presented. The accuracy of the SIR model for the spring 2020 wave of COVID-19 epidemic forecast in Russia is analyzed. As an alternative to modeling the epidemic spread using the SIR model, a new CIR discrete stochastic model is proposed based on the balance of the epidemic indicators at the current and past time points. The new model describes the dynamics of the total number of cases (C), the total number of recoveries and deaths (R), and the number of active cases (I). The system parameters are the percentage increase in the C(t) value and the characteristic of the dynamic balance of the epidemiological process, first introduced in this paper. The principle of the dynamic balance of epidemiological process assumes that any process has the property of similarity between the value of the total number of cases in the past and the value of the total number of recoveries and deaths at present. To calculate the values of the dynamic balance characteristic, an integer linear programming problem is used. In general, the dynamic characteristic of the epidemiological process is not constant. An epidemiological process the dynamic characteristic of which is not constant is called non-stationary. To construct mid-term forecasts of indicators of the epidemiological process at intervals of stationarity of the epidemiological process, a special algorithm has been developed. The question of using this algorithm on the intervals of stationarity and non-stationarity is being examined. Examples of the CIR model application for making forecasts of the considered indicators for the epidemic in Russia in May-June 2020 are given.
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3. Moftakhar L., Seif M., Safe M.S. Exponentially increasing trend of infected patients with COVID-19 in Iran: a comparison of neural network and ARIMA forecasting models. Iran Journal of Public Health. vol. 49. pp. 92–100.
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19. Chinazzi M., Davis J.T., et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020. vol. 368. no. 6489. pp. 395–400.
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24. Krivorotko O.I., Kabanikhin S.I., Zyatkov N.Y., Prikhodko A.Y., Prokhoshin N.M., Shishlenin M.A. Matematicheskoe modelirovanie i prognozirovanie COVID-19 v Moskve i Novosibirskoj oblasti. [Mathematical modeling and prediction of COVID-19 in Moscow city and Novosibirsk region]. Available at: https://arxiv.org/abs/2006.12619v1 (accessed: 30.04.2021) (In Russ.).
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33. Kondratyev M.A. Metody prognozirovanija i modeli rasprostranenija zabolevanij. Komp'juternye issledovanija i modelirovanie. [Forecasting methods and models of disease spread]. Computer Research and Modeling. 2013. vol. 5. no. 5. pp. 863–882.
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39. Jung S., et al. Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases. J. Clin. Med. 2020. vol. 9. no. 2.
40. Cooper I., Mondal A., Antonopoulos C. G. A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons and Fractals. 2020. vol. 139.
Published
2021-10-01
How to Cite
Zakharov, V., & Balykina, Y. (2021). Balance Model of COVID-19 Epidemic Based on Percentage Growth Rate. Informatics and Automation, 20(5), 1034-1065. https://doi.org/10.15622/20.5.2
Section
Mathematical Modeling and Applied Mathematics
Copyright (c) Виктор Захаров, Юлия Балыкина
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