V. Ya. Kisselevskaya-Babinina, A. A. Romanyukha, T. E. Sannikova, “Mathematical model of COVID-19 course and severity prediction”, Mat. Model., 35:5 (2023), 31–46; Math. Models Comput. Simul., 15:6 (2023), 987

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Matematicheskoe modelirovanie, 2023, Volume 35, Number 5, Pages 31–46
DOI: https://doi.org/10.20948/mm-2023-05-03 (Mi mm4461)

This article is cited in 5 scientific papers (total in 5 papers)

Mathematical model of COVID-19 course and severity prediction

V. Ya. Kisselevskaya-Babinina^ab, A. A. Romanyukha^b, T. E. Sannikova^b

^a Lomonosov Moscow State University
^b Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences

Full-text PDF (588 kB) Citations (5)

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DOI: https://doi.org/10.20948/mm-2023-05-03

Abstract: The objective of this study is to develop a method for infection severity predicting and for choosing respiratory support treatment in COVID-19 patients. The tasks of classifying the initial condition and course of the disease in patients with COVID-19 infection and development of a mathematical model for COVID-19 progression in patients admitted in the intensive care unit are being solved. This study analyzes the anamnesis data, assesses the impact of patient’s comorbid chronic diseases and age on the severity of COVID-19 and the effectiveness of treatment. A mathematical model for COVID-19 progression was developed. Model parameters for groups of patients with different chronic diseases were estimated. The comorbidity index has been adapted to the features of the clinical data. An approach to selecting the efficient method of respiratory support in patients with severe forms of COVID-19 infection is proposed.

Keywords: COVID-19, statistical analysis, mathematical modelling, Markov process, comorbidity.

Received: 16.06.2022
Revised: 10.11.2022
Accepted: 06.03.2023

English version:
Mathematical Models and Computer Simulations, 2023, Volume 15, Issue 6, Pages 987–998
DOI: https://doi.org/10.1134/S2070048223060121

Document Type: Article

Language: Russian

Citation: V. Ya. Kisselevskaya-Babinina, A. A. Romanyukha, T. E. Sannikova, “Mathematical model of COVID-19 course and severity prediction”, Mat. Model., 35:5 (2023), 31–46; Math. Models Comput. Simul., 15:6 (2023), 987–998

Citation in format AMSBIB

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\by V.~Ya.~Kisselevskaya-Babinina, A.~A.~Romanyukha, T.~E.~Sannikova

\paper Mathematical model of COVID-19 course and severity prediction

\jour Mat. Model.

\yr 2023

\vol 35

\issue 5

\pages 31--46

\mathnet{http://mi.mathnet.ru/mm4461}

\crossref{https://doi.org/10.20948/mm-2023-05-03}

\transl

\jour Math. Models Comput. Simul.

\yr 2023

\vol 15

\issue 6

\pages 987--998

\crossref{https://doi.org/10.1134/S2070048223060121}

Linking options:

https://www.mathnet.ru/eng/mm4461

https://www.mathnet.ru/eng/mm/v35/i5/p31

This publication is cited in the following 5 articles:

I. D. Kolesin, E. M. Zhitkova, “Matematicheskoe modelirovanie rasprostraneniya COVID-19 s uchetom raspredeleniya bessimptomnykh sluchaev mezhdu deistvitelno bessimptomnymi i predsimptomnymi”, Matem. biologiya i bioinform., 19:1 (2024), 52–60
A. Yu. Perevaryukha, “Phenomenological models of three scenarios of local coronavirus epidemics”, Math. Models Comput. Simul., 16:3 (2024), 396–411
A. V. Demidova, O. V. Druzhinina, O. N. Masina, A. A. Petrov, “Constructing compartmental models of dynanic systems using a software package for symbolic computation in Julia”, Programmirovanie, 2024, no. 2, 33
A. V. Demidova, O. V. Druzhinina, O. N. Masina, A. A. Petrov, “Constructing Compartmental Models of Dynamic Systems Using a Software Package for Symbolic Computation in Julia”, Program Comput Soft, 50:2 (2024), 138
I. V. Derevich, A. A. Panova, “Modeling the spread of viral infection in a local atmosphere infected with SARS-COV-2 virus. Constant virion concentration”, Math. Models Comput. Simul., 16:5 (2024), 698–710

Citing articles in Google Scholar: Russian citations, English citations
Related articles in Google Scholar: Russian articles, English articles

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