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Review of the Mean Field Models for Predicting the Spread of Viral Infections Full article

Conference IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics, and Biomedicine
28-29 Sep 2023 , Новосибирск/Екатеринбург
Source Труды 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB)
Compilation, IEEE. 2023.
Output data Year: 2023, Article number : 10329859, Pages count : 6 DOI: 10.1109/csgb60362.2023.10329859
Tags epidemiological models, mean field game, mean f ield control, SIR model, optimal control
Authors Petrakova Viktoriya 1 , Krivorotko Olga 2 , Neverov Andrei 3
Affiliations
1 Department of Computational Mathematics, Institute of Computational Modeling SB RAS, Krasnoyarsk, Russia
2 Department of Applied Inverse Problems, Sobolev Institute of Mathematics SB RAS, Novosibirsk, Russia
3 International Mathematical Center, Sobolev Institute of Mathematics SB RAS, Novosibirsk, Russia

Funding (2)

1 Sobolev Institute of Mathematics FWNF-2022-0009
2 Russian Science Foundation 23-71-10068

Abstract: Models for the dynamics and control of disease spread are now developing rapidly and play a central role in making decisions about the introduction of various antiviral measures. One of the approaches to solving such problems may be the use of mean field models, which, on the one hand, allow to take into account some stochastic processes occurring in the population and, on the other hand, are expressed in the form of systems with a small number of equations, the solution of which does not require large computing power. This work is devoted to a review of mean field models now used to solve epidemiological problems, as well as a comparison of four specific approaches that can be formulated within the framework of mean field theory.
Cite: Petrakova V. , Krivorotko O. , Neverov A.
Review of the Mean Field Models for Predicting the Spread of Viral Infections
In compilation Труды 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB). – IEEE., 2023. – C.45-50. DOI: 10.1109/csgb60362.2023.10329859 Scopus OpenAlex
Dates:
Published print: Dec 4, 2023
Published online: Dec 4, 2023
Identifiers:
Scopus: 2-s2.0-85180364376
OpenAlex: W4389314556
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