|
This article is cited in 6 scientific papers (total in 6 papers)
Mathematical model of COVID-19 course and severity prediction
V. Ya. Kisselevskaya-Babininaab, A. A. Romanyukhab, T. E. Sannikovab a Lomonosov Moscow State University
b Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences
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
Citation:
V. Ya. Kisselevskaya-Babinina, A. A. Romanyukha, T. E. Sannikova, “Mathematical model of COVID-19 course and severity prediction”, Matem. Mod., 35:5 (2023), 31–46; Math. Models Comput. Simul., 15:6 (2023), 987–998
Linking options:
https://www.mathnet.ru/eng/mm4461 https://www.mathnet.ru/eng/mm/v35/i5/p31
|
|