Mathematical model of COVID-19 course and severity prediction

V. Ya. Kisselevskaya-Babinina ; A. A. Romanyukha ; T. E. Sannikova

Matematičeskoe modelirovanie, Tome 35 (2023) no. 5, pp. 31-46.

Résumé

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.

Détail

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

Bibliographie

[1] J. Hua et al, “Invasive mechanical ventilation in COVID-19 patient management: the experience with 469 patients in Wuhan”, Critical Care, 24:1 (2020), 1–3 | DOI

[2] L. S. Menga et al, “Noninvasive respiratory support for acute respiratory failure due to COVID-19”, Current opinion in critical care, 28:1 (2022), 25 pp. | DOI

[3] M. Antonelli et al, “Predictors of failure of noninvasive positive pressure ventilation in patients with acute hypoxemic respiratory failure: a multi-center study”, Intensive care medicine, 27:11 (2001), 1718–1728 | DOI

[4] S. Baker, W. Xiang, I. Atkinson, “Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach”, Sci. Reports, 10:1 (2020), 1–12

[5] P. J.H. Hulshof et al, “Taxonomic classification of planning decisions in health care: a struc-tured review of the state of the art in OR/MS”, Health systems, 1:2 (2012), 129–175 | DOI

[6] I. M. Longini Jr. et al, “Statistical analysis of the stages of HIV infection using a Markov model”, Statistics in medicine, 8:7 (1989), 831–843 | DOI | MR

[7] A. Moran et al, “Future cardiovascular disease in China: Markov model and risk factor scenario projections from the coronary heart disease policy model-China”, Circulation: Cardiovascular Quality and Outcomes, 3:3 (2010), 243–252 | DOI

[8] M. S.R. Frausto et al, “The dynamics of disease progression in sepsis: Markov modeling describing the natural history and the likely impact of effective antisepsis agents”, Clinical infectious diseases, 27:1 (1998), 185–190 | DOI

[9] L. Peelen et al, “Using hierarchical dynamic Bayesian networks to investigate dynamics of organ failure in patients in the Intensive Care Unit”, J. of biomedical informatics, 43:2 (2010), 273–286 | DOI

[10] C. A. King et al, “Designing and validating a Markov model for hospital-based addiction consult service impact on 12-month drug and non-drug related mortality”, PloS one, 16:9 (2021), e0256793 | DOI

[11] D. IU. Belousov i dr., “Prognozirovanie vliianiia statinov na priamye meditsinskie zatraty pri vtorichnoj profilaktike u patsientov s vysokim riskom razvitiia serdechno-sosudistykh zabolevanij”, Kachestvennaia klinicheskaia praktika, 2011, no. 1, 97–115

[12] N. D. Yushchuk i dr., “Bremia virusnykh gepatitov v Rossiiskoi Federatsii i puti ego snizheniia v dolgosrochnoi perspektive (na primere gepatita S)”, Terapevticheskii arkhiv, 85:12 (2013), 79–85

[13] A. V. Rudakova i dr., “Farmakoekonomicheskie aspekty vaktsinatsii protiv papilloma-virusnoi infektsii devochek-podrostkov v Rossiiskoi Federatsii”, Pediatricheskaia farmakologiia, 14:6 (2017), 494–500 | DOI

[14] D. Hazard et al, “Joint analysis of duration of ventilation, length of intensive care, and mortality of COVID-19 patients: a multistate approach”, BMC medical research methodology, 20:1 (2020), 1–9 | DOI

[15] V. Ia. Kiselevskaia-Babinina, K.A. Popugaev, V.A. Molodov, I.V. Kiselevskaya-Babinina, “Ispolzovanie resursov infekcionnogo koechnogo fonda v period epidemii COVID-19 v zavisimosti ot kharakteristik patsientov”, Neotlozhnaia meditsinskaia pomoshch, 12:2 (2023)

[16] M. E. Charlson et al, “A new method of classifying prognostic comorbidity in longitudinal studies: development and validation”, J. of chronic diseases, 40:5 (1987), 373–383 | DOI

[17] W. D'Hoore, C. Sicotte, C. Tilquin, “Risk adjustment in outcome assessment: the Charlson comorbidity index”, Methods of information in medicine, 32:05 (1993), 382–387 | DOI

[18] M. Charlson et al, “The Charlson comorbidity index can be used prospectively to identify patients who will incur high future costs”, PloS one, 9:12 (2014), e112479 | DOI

[19] A. V. Molochkov i dr., “Komorbidnye zabolevaniia i prognozirovanie iskhoda COVID-19: rezultaty nabliudeniia 13585 bolnykh, nakhodivshikhsia na statsionarnom lechenii v bolnitsakh Moskovskoi oblasti”, Almanakh klinich. medits., 48:S1 (2020), 1–10 | MR

[20] X. Wang et al, “Comorbid chronic diseases and acute organ injuries are strongly correlated with disease severity and mortality among COVID-19 patients: a systemic review and meta-analysis”, Research, 2020 (2020) | Zbl

[21] L. Liu et al, “A simple nomogram for predicting failure of non-invasive respiratory strategies in adults with COVID-19: a retrospective multicentre study”, The Lancet Digital Health, 3:3 (2021), e166–e174 | DOI

[22] Y. Suhov, M. Kelbert, Markov Chains: A Primer in Random Processes and their Applications, Cambridge University Press, 2008, 504 pp. | Zbl

[23] A. Agresti, Categorical data analysis, John Wiley Sons, 2003 | MR

[24] B. Ripley, W. Venables, “Package 'nnet'”, R package version, 7, no. 3–12 (2016), 700 pp.

[25] W. Venables, B. D. Ripley, Modern applied statistics with S, Fourth edition, Springer, 2002 | MR | Zbl

[26] B. Efron, R. J. Tibshirani, An introduction to the bootstrap, CRC press, 1994 | MR

[27] B. Hu et al, “Characteristics of SARS-CoV-2 and COVID-19”, Nature Reviews Microbiology, 19:3 (2021), 141–154 | DOI

[28] A. E. Ivanova, N. B. Pavlov, A. Iu. Mikhailov, “Tendentsii i regionalnye osobennosti zdorovia vzroslogo naseleniia Rossii”, Sotsialnye aspekty zdorovia naseleniia, 19:3 (2011), 25

[29] V. Yu. Semenov, “Zabolevaemost naseleniia Rossiiskoi Federatsii: geograficheskie osobennosti”, Problemy sotsialnoi gigieny, zdravookhraneniia i istorii meditsiny, 23:6 (2015), 6–9

[30] A. K. Singh et al, “Molnupiravir in COVID-19: A systematic review of literature”, Diabetes Metabolic Syndrome: Clinical Research Reviews, 15:6 (2021), 102329 | DOI | MR

[31] G. Fink et al, “Inactivated trivalent influenza vaccination is associated with lower mortality among patients with COVID-19 in Brazil”, BMJ evidence-based medicine, 26:4 (2021), 192–193 | DOI