Modeling the dynamics of key integrated indicators for the COVID-19 spread in St. Petersburg
https://doi.org/10.47619/2713-2617.zm.2023.v.4i1;83-89
Abstract
The author carried out the modeling of the dynamics of key integrated indicators for the COVID-19 outbreak in St. Petersburg, Russia. The dynamics of infection, recovery and mortality in the region was analyzed based on the monitoring data of the Coordination Council to control the incidence of the novel coronavirus infection in St. Petersburg. The analysis showed that it was possible to quickly make a shortterm forecast for the pandemic spread based on the polynomial regression of integral indicators. Through forecasting, administrative and sanitary institutions have the possibility to make suitable management decisions on the creation of normal conditions for maintaining the public health.
About the Author
P. V. Gerasimenko
Emperor Alexander I St. Petersburg State Transport University
Russian Federation
Russian Federation
Petr V. Gerasimenko – Dr. Sci. in Technical Sciences, Professor, Professor of the Economics and Management in Construction Academic Department
9, Moskovsky pr., 190031, St. Petersburg
References
1. Matveev A.V. Mathematical modeling of evaluating the effectiveness of measures against the spread of the COVID-19 epidemic // National Security and Strategic Planning. – 2020. – No. 1 (29). – P. 23–39.
2. Linka K., Peirlinck M., Kuhl E. The reproduction number of COVID-19 and its correlation with public health interventions // Computation Mathematics. – 2020. – Vol. 7. – P. 1035–1050. DOI: 10.1101/2020.05.01.20088047
3. Assessing the impact of non-pharmaceutical interventions (NPI) on the dynamics of COVID-19: A mathematical modeling study in the case of Ethiopia / B. A. Ejigu, M.D. Asfaw, L. Cavalerie, T. Abebaw, M. Nanyingi, M. Baylis // medRxiv. – 2020. – P. 30. DOI: 10.1101/2020.11.16.20231746
4. Spatio-temporal propagation of COVID-19 pandemics / B. Gross, Z. Zheng, S. Liu, X. Chen, A. Sela, J. Li, D. Li, S. Havlin // medRxiv. – 2020. – Vol. 9. – P. 6. DOI: 10.1101/2020.03.23.20041517
5. Yesilkanat C. M. Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm // Chaos, Solitons and Fractals. – 2020. – Vol. 140. – P. 110210. DOI: 10.1016/j.chaos.2020.110210
6. Population flow drives spatio-temporal distribution of COVID-19 in China / J.S. Jia, X. Lu, Y. Yuan, G. Xu, J. Jia, N.A. Christakis // Nature. – 2020. – Vol. 582. – P. 389–394.
7. Spatio-temporal modeling of the COVID-19 epidemic / V.L. Sokolovsky, G. B. Furman, D. A. Polyanskaya, E.G. Furman // Health risk analysis. – 2021. – No. 1. – P. 23–37. DOI: 10.21668/health.risk/2021.1.03
8. Gerasimenko P.V. Modeling and forecasting indicators of the dynamics of the disease of residents of the regions with coronavirus COVID-19 / P. V. Gerasimenko // Transport systems and technologies. – 2020. – V. 6. – No. 4. – P. 88–97. doi:10.17816/transsyst20206488-97
9. Gerasimenko PV, Modeling the number of cases of COVID-19 coronavirus in St. Petersburg in the period 2020–2022. // City Healthсare. 2022; 3(3):30–38 doi:10.47619/2713-2617.zm.2022.v.3i3;30–38
10. Verteshev S. M., Gerasimenko P. V., Lekhin S. N. The role of mathematics and informatics in the training of engineers for innovation // Prospects for the development of higher education: materials of the X International scientific and methodological conference. – Grodno: GSAU, 2017 – S. 223-226.
11. Gerasimenko P. V., Khodakovsky V. A. Introduction to econometrics. Tutorial. – St. Petersburg: PGUPS, 2005. – P. 60.
12. Coronavirus (COVID-19) spread statistics. Online service https://coronavirus-tracking.ru/. (date of access: 20.08.2022
Review
For citations:
Gerasimenko P.V. Modeling the dynamics of key integrated indicators for the COVID-19 spread in St. Petersburg. City Healthcare. 2023;4(1):83-89. (In Russ.) https://doi.org/10.47619/2713-2617.zm.2023.v.4i1;83-89
Views:
104
Email this article 