Geodesic
Mathematical model of COVID-19 course and severity prediction
Matematičeskoe modelirovanie, Tome 35 (2023) no. 5, pp. 31-46

Voir la notice de l'article provenant de la source Math-Net.Ru

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. 
@article{MM_2023_35_5_a2,
     author = {V. Ya. Kisselevskaya-Babinina and A. A. Romanyukha and T. E. Sannikova},
     title = {Mathematical model of {COVID-19} course and severity prediction},
     journal = {Matemati\v{c}eskoe modelirovanie},
     pages = {31--46},
     publisher = {mathdoc},
     volume = {35},
     number = {5},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MM_2023_35_5_a2/}
}
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V. Ya. Kisselevskaya-Babinina; A. A. Romanyukha; T. E. Sannikova. Mathematical model of COVID-19 course and severity prediction. Matematičeskoe modelirovanie, Tome 35 (2023) no. 5, pp. 31-46. http://geodesic.mathdoc.fr/item/MM_2023_35_5_a2/