TY - JOUR

T1 - Data-driven regularization of inverse problem for SEIR-HCD model of COVID-19 propagation in Novosibirsk region

AU - Krivorotko, O.

AU - Zyatkov, N. Y.

N1 - Funding Information: The work is supported by the framework of a grant, following the results of the competition of the mayor’s office of the city of Novosibirsk for the provision of grants in the form of subsidies in the field of scientific and innovative activities. Publisher Copyright: © 2022. Eurasian Journal of Mathematical and Computer Applications. All Rights Reserved.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - epidemiology

KW - compartment modeling

KW - basic reproduction number

KW - COVID-19

KW - inverse problem

KW - regularization

KW - IDENTIFICATION

KW - SPREAD

KW - Basic reproduction number

KW - Covid-19

KW - Epidemiology

KW - Inverse problem

KW - Compartment modeling

KW - Regularization

UR - http://www.scopus.com/inward/record.url?scp=85129918202&partnerID=8YFLogxK

U2 - 10.32523/2306-6172-2022-10-1-51-68

DO - 10.32523/2306-6172-2022-10-1-51-68

M3 - Article

VL - 10

SP - 51

EP - 68

JO - Eurasian Journal of Mathematical and Computer Applications

JF - Eurasian Journal of Mathematical and Computer Applications

SN - 2306-6172

IS - 1

ER -