DATA-DRIVEN REGULARIZATION OF INVERSE PROBLEM FOR SEIR-HCD MODEL OF COVID-19 PROPAGATION IN NOVOSIBIRSK REGION

Abstract The inverse problem for SEIR-HCD model of COVID-19 propagation in Novosi- birsk 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 ini- tial 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 ap- proach 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.