Comparative analysis of the mathematical models of the dynamics of the coronavirus COVID-19 epidemic development in the different countries

Pavel Khrapov, Anastasia Loginova

Abstract


In this work, mathematical modelling of the dynamics of coronavirus COVID-19 is performed for the following countries: the USA, Germany, the UK and the Russian Federation. Cases of COVID-19 virus have been detected in nearly 200 countries. On February 11, 2020, the World Health Organization decided to officially name the virus SARSCoV-2, and the disease caused by this virus - COVID-19 On March 11, WHO Director-General Tedros Gebreyesus announced that the spread of coronavirus infection COVID-19 “could be described as a pandemic.” Viral incubation period of this virus is quite long, it ranges from 1 to 14 days. The danger of a new disease also lies in the fact that it is easily confused with a common cold or flu. Therefore, the spread of coronavirus COVID-19 is a serious threat for international health and economics. A mathematical description of the dynamics of virus allows to study the nature of the disease thoughtfully, to analyze statistical and model data, to make hypotheses concerning the future dynamics of coronavirus and to evaluate the effectiveness of the measures undertaken. For mathematical modelling of coronavirus COVID-19, the authors use a modified system of differential equations constructed according to the SIR compartmental model. The optimal values of the model parameters, that describe the statistical data precisely, were found. The analysis of the current situation of the COVID-19 coronavirus epidemic in each considering country was made, which led to the efficiency mark of the existing measures to struggle against the virus in the USA, Germany, the UK and the Russian Federation.

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References


Makarov V.V., Khromov A.V., Guschin V.A., Tkachuk A.P. Emergence of new infections in the 21st century and identification of pathogens using next generation sequencing. Bulletin of Russian State Medical University. 2017; (1): 5-23. DOI: 10.24075/brsmu.2017-01-01 (In Russian)

Bolles, M., Donaldson, E., Baric, R. SARS-CoV and emergent coronaviruses: viral determinants of interspecies transmission. Current opinion in virology. 2011; 1(6): 624-634. DOI: 10.1016/j.coviro.2011.10.012

Al-Hazmi A. Challenges presented by MERS corona virus and SARS corona virus to global health. Saudi Journal of Biological Sciences. 2016; 23(4): 507-511. DOI: 10.1016/j.sjbs.2016.02.019

Petrosillo, N., Viceconte, G., Ergonul, O., Ippolito, G., Petersen, E. (2020). COVID-19, SARS and MERS: are they closely related? Clinical Microbiology and Infection. 2020; DOI: https://doi.org/10.1016/j.cmi.2020.03.026

Hui D. S., Azhar E. EI, Madani T. A., Ntoumi, F., Kock R.; Dar O., Ippolito G., Mchugh T, D., Memish Z. A. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health — The latest 2019 novel coronavirus outbreak in Wuhan, China. International Journal of Infectious Diseases: journal. 2020, 91: 264-266. DOI: 10.1016/j.ijid.2020.01.009

Wang D., Hu B., Hu C., et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA. Published online February 07, 2020. DOI: 10.1001/jama.2020.1585

Prevention, Treatment of Novel Coronavirus (2019-nCoV) [Online]. Available: https://www.cdc.gov/coronavirus/2019-ncov/about/prevention-treatment.html

Heymann D. L., Shindo N. COVID-19: what is next for public health? The Lancet. 2020. DOI: 10.1016/S0140-6736(20)30374-3

Read J. M., Bridgen J. RE, Cummings D. AT, Ho A., Jewell C. P. Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions. 2020. DOI: 10.1101/2020.01.23.20018549

Kucharski A., Russell T., Diamond C., Liu Y., Edmunds J., Funk S., Eggo R., CMMID nCoV working group. Analysis and projections of transmission dynamics of nCoV in Wuhan [Online]. Available: https://cmmid.github.io/ncov/wuhan_early_dynamics/

Roosa K., Lee Y., Luo R., Kirpich A., R. Rothenberg, Hyman J.M., Yan P., Chowell G. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling. 2020; 5:256-263. DOI: 10.1016/j.idm.2020.02.002

Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., ... Davies, N. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The lancet infectious diseases. 2020; DOI: https://doi.org/10.1016/S1473-3099(20)30144-4

Chintalapudi, N., Battineni, G., Amenta, F. COVID-19 disease outbreak forecasting of registered and recovered cases after sixty-day lockdown in Italy: A data driven model approach. Journal of Microbiology, Immunology and Infection. 2020; DOI: https://doi.org/10.1016/j.jmii.2020.04.004

Sarkodie, S. A., Owusu, P. A. Investigating the cases of novel coronavirus disease (COVID-19) in China using dynamic statistical techniques. Heliyon. 2020; DOI: https://doi.org/10.1016/j.heliyon.2020.e03747

Roda, W. C., Varughese, M. B., Han, D., & Li, M. Y. Why is it difficult to accurately predict the COVID-19 epidemic? Infectious Disease Modelling. 2020; https://doi.org/10.1016/j.idm.2020.03.001

Kermack W. O., McKendrick A. G. Contributions to the mathematical theory of epidemics. Proceedings of the Royal Society of Edinburgh, Section A. Mathematics. 1927; 115:700–721. DOI: 10.1098/rspa.1927.0118

Khaleque A. and Sen P. An empirical analysis of the Ebola outbreak in West Africa. Scientific reports. 2017; 7: 42594. DOI: 10.1038/srep42594

Urakova K. A., Khrapov P. V., Mathematical modelling of Ebola hemorrhagic fever epidemiological development in West Africa. Almanakh sovremennoi nauki I obrazovaniya. 2017; 4-5 (118):97-99. Available at: https://elibrary.ru

/item.asp?id=29147514 (accessed 10.01.2019). (In Russian)

Huang X. C., Villasana M. An extension of the Kermack– McKendrick model for AIDS epidemic. Journal of the Franklin Institute. 2005; 342.4: 341-351. DOI: 10.1016/j.jfranklin.2004.11.008

Khrapov N. P., Khrapov P. V., Shumilina A. O. Mathematical Model and forecast of AIDS epidemiological development. Almanakh sovremennoi nauki I obrazovaniya, Gramota. 2008; 12(9):218-221. Available at: http://scjournal.ru/articles/issn_1993- 5552_2008_12_70.pdf. (In Russian)

Khrapov P. V., Loginova A. A. Mathematical modelling of the dynamics of AIDS epidemics development in the world. International Journal of Open Information Technologies. 2019; 7(6): 13-16. Available at: http://injoit.org/index.php/j1/article/view/755/720.

Khrapov P. V., Loginova A. A. Mathematical modelling of the dynamics of the сoronavirus COVID-19 epidemic development in China. International Journal of Open Information Technologies. 2020; 8(4): 13-16. Available at: http://www.injoit.org/index.php/j1/article/view/908/874.

World Health Organization. (2020). Coronavirus disease 2019 (COVID-19): situation report, 72.

Velavan, T. P., Meyer, C. G. The COVID-19 epidemic. Trop Med Int Healthю 2020; 25(3), 278-280.

Heymann, D. L., Shindo, N. COVID-19: what is next for public health? The Lancet. 2020; 395 (10224), 542-545.


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