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Journal of Applied and Industrial Mathematics
Published in: Journal of Applied and Industrial Mathematics 1/2023

01-03-2023

Simulation of COVID-19 Spread Scenarios in the Republic of Kazakhstan Based on Regularization of the Agent-Based Model

Authors: O. I. Krivorotko, S. I. Kabanikhin, M. A. Bektemesov, M. I. Sosnovskaya, A. V. Neverov

Published in: Journal of Applied and Industrial Mathematics | Issue 1/2023

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Abstract

We propose an algorithm for modeling scenarios for newly diagnosed cases of COVID-19 in the Republic of Kazakhstan. The algorithm is based on treating incomplete epidemiological data and solving the inverse problem of reconstructing the parameters of the agent-based model (ABM) using the set of available epidemiological data. The main tool for constructing the ABM is the Covasim open library. In the event of a drastic change in the situation (appearance of a new strain, removal or introduction of restrictive measures, etc.), the model parameters are updated taking into account additional information for the previous month (online data assimilation). The inverse problem is solved by stochastic global optimization (of tree-structured Parzen estimators). As an example, we give two scenarios of COVID-19 propagation calculated on December 12, 2021 for the period up to January 20, 2022. The scenario that took into account the New Year holidays (published on December 12, 2021 on http://​covid19-modeling.​ru ) almost coincided with what happened in reality (the error was 0.2%).
previous article Parametric RANS Simulation of a Cavitation Flow in the Channel of a Control Valve Cage
next article On a Godunov-Type Numerical Scheme for Describing Gas and Dust Components in Star Formation Problems

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Metadata
Title
Simulation of COVID-19 Spread Scenarios in the Republic of Kazakhstan Based on Regularization of the Agent-Based Model
Authors
O. I. Krivorotko
S. I. Kabanikhin
M. A. Bektemesov
M. I. Sosnovskaya
A. V. Neverov
Publication date
01-03-2023
Publisher
Pleiades Publishing
Published in
Journal of Applied and Industrial Mathematics / Issue 1/2023
Print ISSN: 1990-4789
Electronic ISSN: 1990-4797
DOI
https://doi.org/10.1134/S1990478923010118

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