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Bulletin of Abai KazNPU. Series of Physical and mathematical sciences
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Vol. 82 No. 2 (2023)

SOLVING OF SOME INVERSE PROBLEM OF EPIDEMIOLOGY BY STOCHASTIC NATURE-LIKE METHODS

Published June 2023

64

30

S. Kabanikhin+−
Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk State University, Novosibirsk
M. Bektemesov+−
Abai Kazakh National Pedagogycal University, Almaty
Zh. Bektemessov +−
Al-Farabi Kazakh National University, Almaty
Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk State University, Novosibirsk
Abai Kazakh National Pedagogycal University, Almaty
Al-Farabi Kazakh National University, Almaty
DOI: 10.51889/2959-5894.2023.82.2.007
Abstract

The Covid-19 pandemic has shown that there are a number of pressing global problems in the world. In order to prevent the spread of infection as much as possible, it is necessary to comprehensively analyze the dynamics of the disease, calculate the burden on the healthcare sector and make more realistic forecasts using mathematical modeling tools. Several statistical, dynamic and mathematical models, including the SEIRD and SEIR-HCD models, have been used to analyze the dynamics of the Covid-19 outbreak. The improved SEIR-HCD mathematical model compensates for the discrepancy between actual and predicted data. Predicted solutions were obtained using methods for solving inverse problems using stochastic metaheuristic global optimization algorithms to restore the parameters of the mathematical model using publicly available data related to the Covid-19 pandemic, where, in turn, the restored parameter values will help build a medium-term forecast of the spread of an infectious disease in particular selected regions or for the country as a whole.

pdf (Русский)
Keywords
mathematical modeling pandemic inverse problem identification optimization firefly algorithm
Language

Русский

How to Cite

[1]
Кабанихин, С., Бектемесов, М. and Бектемесов, Ж. 2023. SOLVING OF SOME INVERSE PROBLEM OF EPIDEMIOLOGY BY STOCHASTIC NATURE-LIKE METHODS. Bulletin of Abai KazNPU. Series of Physical and mathematical sciences. 82, 2 (Jun. 2023), 64–71. DOI:https://doi.org/10.51889/2959-5894.2023.82.2.007.