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COVID‑19 mathematical forecasting in the Russian Federation

https://doi.org/10.18705/1607-419X-2020-26-3-288-294

Abstract

A new coronavirus infection (CVI) is a challenge to the medical system of the Russian Federation and requires precise flow forecasting to take the necessary measures on time. The article provides an overview of modern mathematical tools for predicting the course of CVI in the world. The created CVI forecasting project office allowed to determine the most effective analysis tools in the Russian Federation — the ARIMA, SIRD and Holt–Winters exponential smoothing models. Implementation of these models allows for prediction of short-term morbidity, mortality and survival of patients with an accuracy of 99 % both in the Russian Federation in general and in the regions. In addition, the distribution of CVI was characterized. Particularly, Moscow and Moscow region have the maximum spread of infection, and other regions are lagging behind in the dynamics of the incidence by 1–3 weeks. The obtained models allow us to predict the course of the disease in the regions successfully and take the necessary measures in a timely manner.

About the Authors

I. A. Lakman
Ufa State Aviation Technical University
Russian Federation

Irina A. Lakman, PhD in Technics, Associate Professor, Department of Computational Mathematics and Cybernetics

Ufa



A. A. Agapitov
Ufa State Aviation Technical University
Russian Federation

Aleksandr A. Agapitov, Junior Researcher

Ufa



L. F. Sadikova
Ufa State Aviation Technical University
Russian Federation

Liana F. Sadikova, Junior Researcher

Ufa



O. V. Chernenko
LLC “Laboratory of Нemodialysis”
Russian Federation

Oleg V. Chernenko, MD, PhD, Deputy Director for Development

Ufa



S. V. Novikov
Ufa State Aviation Technical University
Russian Federation

Sergey V. Novikov, PhD in Economics, Acting Rector

Ufa



D. V. Popov
Ufa State Aviation Technical University
Russian Federation

Denis V. Popov, PhD in Technics, Associate Professor, Department of Computational Mathematics and Cybernetics

Ufa



V. N. Pavlov
Bashkir State Medical University
Russian Federation

Valentin N. Pavlov, MD, PhD, DSc, Professor, Corresponding Member of the Russian Academy of Sciences, Rector

Ufa



D. F. Gareeva
Bashkir State Medical University
Russian Federation

Diana F. Gareeva, MD, PhD, Cardiologist, Assistant, Department of Propaedeutics of Internal Diseases

Ufa



B. T. Idrisov
Bashkir State Medical University
Russian Federation

Bulat T. Idrisov, MD, Assistant, Department of Infectious Diseases

Ufa



A. R. Bilyalov
Bashkir State Medical University
Russian Federation

Azat R. Bilyalov, MD, PhD, Head, Department of Information Technologies, Associate Professor, Department of Traumatology and Orthopedics

Ufa



N. Sh. Zagidullin
Ufa State Aviation Technical University; Bashkir State Medical University
Russian Federation

Naufal Sh. Zagidullin, MD, PhD, DSc, Professor, Director, Research Institute of Cardiology, Head, Department of Propaedeutics of Internal Diseases, Bashkir State Medical University

3 Lenin street, Ufa, 450008



References

1. Roosa K, Lee Y, Luo R, Kirpich A, Rothenberg R, Hyman JM, Yan P et al. Short-term forecasts of the COVID-19 epidemic in Guangdong and Zhejiang, China: February 13–23, 2020. J Clin Med. 2020;9(2):596. doi:10.3390/jcm9020596

2. Stübinger J, Schneider L Epidemiology of coronavirus COVID-19: forecasting the future incidence in different countries. Healthcare. 2020;8(2):99. doi:10.3390/healthcare8020099

3. Huang Y, Yang L, Dai H, Tian F, Chen K. Epidemic situation and forecasting of COVID-19 in and outside China. Bulletin of the World Health Organization. [Published online 16 March 2020]. doi:10.2471/BLT.20.255158

4. Sun D, Duan L, Xiong J, Wang D Modelling and forecasting the spread tendency of the COVID-19 in China. BMC Infectious Diseases. [Published online 8 May 2020]. doi:10.21203/rs.3.rs-26772/v1

5. Avila E, Canto FJA Fitting parameters of SEIR and SIRD models of COVID-19 pandemic in Mexico. [Published online 15 April 2020]. [Electronic resource]. URL: https://www.researchgate.net/publication/341165247_Fitting_parameters_of_SEIR_and_SIRD_models_of_COVID-19_pandemic_in_Mexico#fullTextFileContent

6. Prem K, Liu Y, Russell TW, Kucharski AJ, Eggo RM, Davies N et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. Lancet Public Health 2020;5(5):e261–270. doi:10.1016/S2468-2667(20)30073-6

7. Benvenuto D, Giovanetti M, Vassallo L, Angeletti S, Ciccozzi M Application of the ARIMA model on the COVID-2019 epidemic dataset. Data Brief. 2020:1053403. [Ahead of print, published online 26 February 2020]. doi:10.1016/j.dib.2020.105340

8. Dehesh T, Mardani-Fard HA, Dehesh P Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models. MedRxiv. [Published online 18 March 2020]. doi:10.1101/2020.03.13.20035345

9. Yonar H, Yonar A, Tekindal MA, Tekindal M. Modeling and forecasting for the number of cases of the COVID-19 pandemic with the curve estimation models, the Box-Jenkins and exponential smoothing methods. Euras J Med Oncol. 2020;4(2):160–165. doi:10.14744/ejmo.2020.28273

10. Ribeiro MHDM, Gomes da Silva R, Mariani VC, Coelho Ld S. Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons and Fractals. 2020;135: 109853. doi:10.1016/j.chaos.2020.109853

11. Zhang Z, Wang X, Gong H, Liu X, Chen H, Chu Z et al. Daily tracking and forecasting of the global COVID-19 pandemic trend using holt–winters exponential smoothing. Lancet. [Published online 15 April 2020]. https://dx.doi.org/10.2139/ssrn.3564413

12. Abdulmajeed K, Adeleke M, Popoola L. Online forecasting of COVID-19 cases in Nigeria using limited data. Data Brief. 2020;30:105683. doi:10.1016/j.dib.2020.105683

13. Elmousalami HH, Hassanien AE. Day level forecasting for coronavirus disease (COVID-19) spread: analysis, modeling and recommendations. [Published online 15 March 2020]. [Electronic resource]. URL: https://arxiv.org/ftp/arxiv/papers/2003/2003.07778.pdf

14. Petropoulos F, Makridakis S Forecasting the novel coronavirus COVID-19. PLoS ONE 15(3):e0231236. doi:10.1371/journal.pone.0231236

15. Zagidullin N, Motloch LJ, Gareeva D, Hamitova A, Lakman I, Krioni I et al. Combining novel biomarkers for risk stratification of two-year cardiovascular mortality in patients with ST-elevation myocardial infarction. J Clin Med. 2020;9(2):550. doi:10.3390/jcm9020550


Review

For citations:


Lakman I.A., Agapitov A.A., Sadikova L.F., Chernenko O.V., Novikov S.V., Popov D.V., Pavlov V.N., Gareeva D.F., Idrisov B.T., Bilyalov A.R., Zagidullin N.Sh. COVID‑19 mathematical forecasting in the Russian Federation. "Arterial’naya Gipertenziya" ("Arterial Hypertension"). 2020;26(3):288-294. https://doi.org/10.18705/1607-419X-2020-26-3-288-294

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ISSN 1607-419X (Print)
ISSN 2411-8524 (Online)