Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region

ABSTRACT We investigate inverse problems of finding unknown parameters of mathematical models SEIR-HCD and SEIR-D of COVID-19 spread with additional information about the number of detected cases, mortality, self-isolation coefficient, and tests performed for the city of Moscow and Novosibirsk regio...

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Published inNumerical analysis and applications Vol. 13; no. 4; pp. 332 - 348
Main Authors Krivorot’ko, O. I., Kabanikhin, S. I., Zyat’kov, N. Yu, Prikhod’ko, A. Yu, Prokhoshin, N. M., Shishlenin, M. A.
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 01.10.2020
Subjects
Mathematics
Mathematics and Statistics
Numerical Analysis
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ISSN1995-4239
1995-4247
1995-4247
DOI10.1134/S1995423920040047

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Summary:ABSTRACT We investigate inverse problems of finding unknown parameters of mathematical models SEIR-HCD and SEIR-D of COVID-19 spread with additional information about the number of detected cases, mortality, self-isolation coefficient, and tests performed for the city of Moscow and Novosibirsk region since 23.03.2020. In SEIR-HCD the population is divided into seven groups, and in SEIR-D into five groups with similar characteristics and transition probabilities depending on the specific region of interest. An identifiability analysis of SEIR-HCD is made to reveal the least sensitive unknown parameters as related to the additional information. The parameters are corrected by minimizing some objective functionals which is made by stochastic methods (simulated annealing, differential evolution, and genetic algorithm). Prognostic scenarios for COVID-19 spread in Moscow and in Novosibirsk region are developed, and the applicability of the models is analyzed.
ISSN:1995-4239
1995-4247
1995-4247
DOI:10.1134/S1995423920040047