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Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region. / Krivorot’ko, O. I.; Kabanikhin, S. I.; Zyat’kov, N. Yu et al.

In: Numerical Analysis and Applications, Vol. 13, No. 4, 10.2020, p. 332-348.

Research output: Contribution to journal › Article › peer-review

Harvard

Krivorot’ko, OI, Kabanikhin, SI, Zyat’kov, NY, Prikhod’ko, AY, Prokhoshin, NM & Shishlenin, MA 2020, 'Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region', Numerical Analysis and Applications, vol. 13, no. 4, pp. 332-348. https://doi.org/10.1134/S1995423920040047

APA

Krivorot’ko, O. I., Kabanikhin, S. I., Zyat’kov, N. Y., Prikhod’ko, A. Y., Prokhoshin, N. M., & Shishlenin, M. A. (2020). Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region. Numerical Analysis and Applications, 13(4), 332-348. https://doi.org/10.1134/S1995423920040047

Vancouver

Krivorot’ko OI, Kabanikhin SI, Zyat’kov NY, Prikhod’ko AY, Prokhoshin NM, Shishlenin MA. Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region. Numerical Analysis and Applications. 2020 Oct;13(4):332-348. doi: 10.1134/S1995423920040047

Author

Krivorot’ko, O. I. ; Kabanikhin, S. I. ; Zyat’kov, N. Yu et al. / Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region. In: Numerical Analysis and Applications. 2020 ; Vol. 13, No. 4. pp. 332-348.

BibTeX

@article{fa37ef261b9a482a8c4895a3f0405c9b,
title = "Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region",
abstract = "We investigate inverse problems of finding unknown parameters ofmathematical models SEIR-HCD and SEIR-D of COVID-19 spread withadditional information about the number of detected cases, mortality,self-isolation coefficient, and tests performed for the city of Moscowand Novosibirsk region since 23.03.2020. In SEIR-HCD the population isdivided into seven groups, and in SEIR-D into five groups with similarcharacteristics and transition probabilities depending on the specificregion of interest. An identifiability analysis of SEIR-HCD is made toreveal the least sensitive unknown parameters as related to theadditional information. The parameters are corrected by minimizing someobjective functionals which is made by stochastic methods (simulatedannealing, differential evolution, and genetic algorithm). Prognosticscenarios for COVID-19 spread in Moscow and in Novosibirsk region aredeveloped, and the applicability of the models is analyzed.",
author = "Krivorot{\textquoteright}ko, {O. I.} and Kabanikhin, {S. I.} and Zyat{\textquoteright}kov, {N. Yu} and Prikhod{\textquoteright}ko, {A. Yu} and Prokhoshin, {N. M.} and Shishlenin, {M. A.}",
note = "Publisher Copyright: {\textcopyright} 2020, Pleiades Publishing, Ltd. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2020",
month = oct,
doi = "10.1134/S1995423920040047",
language = "English",
volume = "13",
pages = "332--348",
journal = "Numerical Analysis and Applications",
issn = "1995-4239",
publisher = "Maik Nauka-Interperiodica Publishing",
number = "4",

}

RIS

TY - JOUR

T1 - Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region

AU - Krivorot’ko, O. I.

AU - Kabanikhin, S. I.

AU - Zyat’kov, N. Yu

AU - Prikhod’ko, A. Yu

AU - Prokhoshin, N. M.

AU - Shishlenin, M. A.

N1 - Publisher Copyright: © 2020, Pleiades Publishing, Ltd. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/10

Y1 - 2020/10

N2 - We investigate inverse problems of finding unknown parameters ofmathematical models SEIR-HCD and SEIR-D of COVID-19 spread withadditional information about the number of detected cases, mortality,self-isolation coefficient, and tests performed for the city of Moscowand Novosibirsk region since 23.03.2020. In SEIR-HCD the population isdivided into seven groups, and in SEIR-D into five groups with similarcharacteristics and transition probabilities depending on the specificregion of interest. An identifiability analysis of SEIR-HCD is made toreveal the least sensitive unknown parameters as related to theadditional information. The parameters are corrected by minimizing someobjective functionals which is made by stochastic methods (simulatedannealing, differential evolution, and genetic algorithm). Prognosticscenarios for COVID-19 spread in Moscow and in Novosibirsk region aredeveloped, and the applicability of the models is analyzed.

AB - We investigate inverse problems of finding unknown parameters ofmathematical models SEIR-HCD and SEIR-D of COVID-19 spread withadditional information about the number of detected cases, mortality,self-isolation coefficient, and tests performed for the city of Moscowand Novosibirsk region since 23.03.2020. In SEIR-HCD the population isdivided into seven groups, and in SEIR-D into five groups with similarcharacteristics and transition probabilities depending on the specificregion of interest. An identifiability analysis of SEIR-HCD is made toreveal the least sensitive unknown parameters as related to theadditional information. The parameters are corrected by minimizing someobjective functionals which is made by stochastic methods (simulatedannealing, differential evolution, and genetic algorithm). Prognosticscenarios for COVID-19 spread in Moscow and in Novosibirsk region aredeveloped, and the applicability of the models is analyzed.

UR - http://www.scopus.com/inward/record.url?scp=85097958673&partnerID=8YFLogxK

U2 - 10.1134/S1995423920040047

DO - 10.1134/S1995423920040047

M3 - Article

AN - SCOPUS:85097958673

VL - 13

SP - 332

EP - 348

JO - Numerical Analysis and Applications

JF - Numerical Analysis and Applications

SN - 1995-4239

IS - 4

ER -

ID: 27119928