International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749

COVID-19 Highest Incidence Forecast in Russia Based on Regression Model

Iosif Z. Aronov
Department of Commerce and Trade Regulation, MGIMO (Moscow State Institute of International Relations) University, Moscow, Russia.

Olga V. Maksimova
Department of Global Climate Stabilization Research, Yu. A. Izrael Institute of Global Climate and Ecology, Moscow, Russia.

Nataliia M. Galkina
Department of Trade Barriers Analysis, International Trade and Integration (ITI) Research Center, Moscow, Russia.

DOI https://doi.org/10.33889/IJMEMS.2020.5.5.063

Received on May 08, 2020
  ;
Accepted on June 12, 2020

Abstract

The authors suggest a simple regression model of COVID-19 highest incidence prognosis in Russia on the basis of the revealed correlation between the duration of coronavirus peak (plateau) and air traffic volume. The study base included 37 countries in Europe, South America and Asia. Cluster analysis on the basis of the Euclidean metric for these countries showed the necessity of classifying the USA and China into a separate group, which gave grounds to exclude these countries from the analysis. In addition, Ireland was excluded from the analysis due to its special geographical location. For the remaining countries, the correlation coefficient between the number of airline passengers and the duration of the epidemic before reaching its peak was 0,87, which shows a high level of linear relationship between these indicators. Point forecast for the highest incidence in Russia by regression line falls on the 4th of May. The forecast interval with confidence levelγ=0.9 is ±14 days from the calculated date. The one-way analysis of variance showed that from April 22 to May 2, there was a slowdown in the growth rates of the diseased, which indicates an exit to the plateau.

Keywords- COVID-19, Regression model, Forecast, Peak (plateau).

Citation

Aronov, I. Z., Maksimova, O. V., & Galkina, N. M. (2020). COVID-19 Highest Incidence Forecast in Russia Based on Regression Model. International Journal of Mathematical, Engineering and Management Sciences, 5(5), 812-819. https://doi.org/10.33889/IJMEMS.2020.5.5.063.

Conflict of Interest

The authors confirm that there is no conflict of interest to declare for this publication.

Acknowledgements

The authors sincerely thank anonymous reviewers and the Editor-in-Chief for providing critical comments and suggestions for improving the quality of the paper.

References

Aronov, I., & Maksimova, O. (2020). Life quality and prognosis of COVID-19 peak morbidity. Available at: https://ria-stk.ru/stq/adetail.php?ID=187663.

Chen, T., Rui, J., Wang, Q.P., Zhao, Z.Y., Cui, J.A., & Yin, L. (2020). A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infectious Diseases of Poverty, 9, 24. https://doi.org/10.1186/s40249-020-00640-3.

Ivorra, B., Ferrández, M.R., Vela-Pérez, M., & Ramos, A.M. (2020). Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China. Communications in Nonlinear Science and Numerical Simulation, 88. https://doi.org/10.1016/j.cnsns.2020.105303.

Koo, R.J., Cook, A.R., Park, M., Sun, Y., Sun, H., & Lim, J.T. (2020). Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study. Available at: https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30162-6/fulltext.

Krantz, S.G., & Rao, A.S.S. (2020). Level of underreporting including underdiagnosis before the first peak of COVID-19 in various countries: preliminary retrospective results based on wavelets and deterministic modeling. Infection Control & Hospital Epidemiology, 1-3. https://doi.org/10.1017/ice.2020.116.

Kucharski, A.J., Russell, T.W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., & Davies, N. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet Infectious Diseases, 20(5), 553-558.

Rabajante, J.F. (2020). Insights from early mathematical models of 2019-nCoV acute respiratory disease (COVID-19) dynamics. ArXiv Preprint ArXiv:2002.05296.

Sethy, P.K., Behera, S.K., Ratha, P.K., & Biswas, P. (2020). Detection of coronavirus disease (Covid-19) based on deep features and support vector machine. International Journal of Mathematical, Engineering and Management Sciences, 5(4), 643-651.

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