Abstract: The inverse problem for SEIR-HCD model of COVID-19 propagation in Novosibirsk region described by system of seven nonlinear ordinary differential equations (ODE) is numerical investigated. The inverse problem consists in identification of coefficients of ODE system (infection rate, portions of infected, hospitalized, mortality cases) and some initial conditions (initial number of asymptomatic and symptomatic infectious) by additional measurements about daily diagnosed, critical and mortality cases of COVID-19. Due to ill-posedness of inverse problem the regularization is applied based on usage of additional information about antibodies IgG to COVID-19 and detailed mortality statistics. The inverse problem is reduced to a minimization problem of misfit function. We apply data-driven approach based on combination of global (OPTUNA software) and gradient-type methods for solving the minimization problem. The numerical results show that adding new information and detailed statistics increased the forecasting scenario in 2 times.

Cite: Krivorotko O.I. , Zyatkov N.Y.
Data-driven regularization of inverse problem for SEIR-HCD model of covid-19 propagation in Novosibirsk region
Eurasian Journal of Mathematical and Computer Applications. 2022. V.10. N1. P.51-68. DOI: 10.32523/2306-6172-2022-10-1-51-68 WOS Scopus РИНЦ OpenAlex

Dates:

Submitted:	Dec 19, 2021
Accepted:	Feb 6, 2022
Published print:	Jun 7, 2022
Published online:	Jun 7, 2022

Identifiers:

Web of science:	WOS:000774219600004
Scopus:	2-s.2-85129918202
Elibrary:	48584566
OpenAlex:	W4220900595

Citing:

DB	Citing
Web of science	7
OpenAlex	10

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