Estimation of SIRM Model's Nonstationary Parameters of COVID-19 in Czech Rebuplic

Nakonechnyi, O.; Shevchuk, I.; Rusina, N.; Vergunova, I.; Myrvoda, S.; Vertelko, M.

Nakonechnyi, O.; Shevchuk, I.; Rusina, N.; Vergunova, I.; Myrvoda, S.; Vertelko, M..

3rd IEEE International Conference on System Analysis and Intelligent Computing, SAIC 2022 ; 2022.

Article in English | Scopus | ID: covidwho-2136477

ABSTRACT

ABSTRACT

This paper is a comprehensive study dedicated to practical solution of estimation problems in models of the spread of infectious diseases. Mentioned algorithms of parameters' estimation make possible to build mathematical models of the spread of infectious diseases based on observations. The results of analysis of the approach to mathematical modeling of the spread of infectious diseases are given in this paper, in particular simulation models and estimation methods in models of population dynamics. Computer simulation for analysis of COVID-19 pandemic in Czech Republic demonstrates efficiency o f t he mentioned algorithm. © 2022 IEEE.

Keywords

estimate of nonstationary parameters; mathematical nonlinear models; the spread of infection; Parameter estimation; Estimate of nonstationary parameter; Estimation problem; Infectious disease; Mathematical nonlinear model; Non-linear modelling

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 3rd IEEE International Conference on System Analysis and Intelligent Computing, SAIC 2022 Year: 2022 Document Type: Article

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