MATHEMATICAL MODEL OF THE EPIDEMIC PROPAGATION WITH LIMITED TIME SPENT IN EXPOSED AND INFECTED COMPARTMENTS
DOI:
https://doi.org/10.26577/JMMCS.2021.v112.i4.14Keywords:
epidemic, mathematical model, COVIDAbstract
A discrete nonlinear mathematical model of the epidemic development is proposed. It involves dividing the population into eight compartments (susceptible, exposed, asymptomatic, easily sick, hospitalized, critically ill, recovered and deceased). At the same time, the time spent in compartments of exposed and all forms of patients is considered limited. Thus, any person who has been in contact with an infected person, after a while, either gets sick or does not, leaving the exposed compartment, and any patient, over time, for sure, either goes to the group of more severe patients, dies or recovers. This deterministic model is presented in a discrete form and simulates the quantitative change of various groups by day during the spread of the epidemic. It is a transformation of the SEIR model. The article also presents a numerical analysis of the proposed model. The development of the COVID epidemic in Kazakhstan is considered as an example. At the end, forecasts are given based on preliminary data from the first months of quarantine. Various parameters of the model when starting numerical experiments were found based on computational experiments. At the same time, for a given deterministic one, the effect of wavelike changes in the number of infected is observed.
References
[2] Bailey, N. The mathematical theory of infectious diseases and its applications (2nd ed.). London, Griffin, 1975.
[3] Bacaer, N. Le Modele Stochastique SIS pour une Epiddmie dans un Environnement Aleatoire. – J. of Mathematical Biology, 2016, v. 73, 847–866.
[4] Wang, X. An SIRS Epidemic Model with Vital Dynamics and a Ratio-Dependent Saturation Incidence Rate. – Discrete Dynamics in Nature and Society. 2015. https://doi.org/10.1155/2015/720682
[5] Mwalili, S., Kimathi, M., Ojiambo, V. et al. SEIR model for COVID-19 dynamics incorporating the environment and social distancing. – BMC Res Notes 13, 352,2020. https://doi.org/10.1186/s13104-020-05192-1
[6] Wang, J. Analysis of an SEIS Epidemic Model with a Changing Delitescence. – Abstract and Applied Analysis, 2012, 4. https://www.hindawi.com/journals/aaa/2012/318150/
[7] R. Sameni Mathematical Modeling of Epidemic Diseases; A Case Study of the COVID-19 Coronavirus. – arXiv:2003.11371. 2020.
[8] Криворотько О.И., Кабанихин С.И., Зятьков Н.Ю., Приходько А.Ю., Прохошин Н.М., Шишленин М.А. Математическое моделирование и прогнозирование COVID-19 в Москве и Новосибирской области. – 2020 https://arxiv.org/pdf/2006.12619.pdf
[9] Hethcote, H.W. The Mathematics of Infectious Diseases. – SIAM Review 42, 599–653, 2000.
[10] Almeida, R., Cruz, A., Martins, N., and Monteiro N. An epidemiological MSEIR Model Described by the Caputo Fractional Derivative. – Int. J. of Dynamics and Control, 2019, 7, 776–784.
[11] Unlu, E., Leger, H. Motornyi, O. et al Epidemic Analysis of COVID-19 Outbreak and Counter-Measures in France. – 2020. medRxiv. 2020.04.27.20079962. DOI: 10.1101/2020.04.27.20079962.
[12] Greenhalgh, D. and Das R. Modeling Epidemics with Variable Contact Rates. – Theoretical Population Biology, 1995, 2 (47), 129-179.
[13] Huang, H. and Wang, M. The Reaction-Diffusion System for an SIR Epidemic Model with a Free Boundary. – Discrete and Continuous Dynamical Systems. Series B, 2015, 20(7), 2039-2050.
[14] Gao, S., Teng, Z., Nieto, J. and Torres, A. Analysis of an SIR Epidemic Model with Pulse Vaccination and Distributed Time Delay. – Journal of Biomedicine and Biotechnology. 2007, 64870. doi:10.1155/2007/64870. PMC 2217597. PMID 18322563.
[15] May, R. and Anderson, R. Infectious diseases of humans: dynamics and control. – Oxford: Oxford University Press, 1991.
[16] Brauer, F. et al. Mathematical Epidemiology. Eds. – Springer, 2008.
[17] Capasso, V. Mathematical Structures of Epidemic Systems. 2nd Printing. – Heidelberg, Springer, 2008.
[18] Vynnycky, E. and White, R.G., eds. An Introduction to Infectious Disease Modelling. – Oxford: Oxford University Press, 2010.
[19] Diekmann, O., Heesterbeek, H. and Britton, T. Mathematical Tools for Understanding Infectious Disease Dynamics. – Princeton Series in Theoretical and Computational Biology. Princeton University Press, Princeton, 2013.