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Optimization Methods for Solving Inverse Immunology and Epidemiology Problems. / Kabanikhin, S. I.; Krivorotko, O. I.

In: Computational Mathematics and Mathematical Physics, Vol. 60, No. 4, 01.04.2020, p. 580-589.

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

Harvard

Kabanikhin, SI & Krivorotko, OI 2020, 'Optimization Methods for Solving Inverse Immunology and Epidemiology Problems', Computational Mathematics and Mathematical Physics, vol. 60, no. 4, pp. 580-589. https://doi.org/10.1134/S0965542520040107

APA

Kabanikhin, S. I., & Krivorotko, O. I. (2020). Optimization Methods for Solving Inverse Immunology and Epidemiology Problems. Computational Mathematics and Mathematical Physics, 60(4), 580-589. https://doi.org/10.1134/S0965542520040107

Vancouver

Kabanikhin SI, Krivorotko OI. Optimization Methods for Solving Inverse Immunology and Epidemiology Problems. Computational Mathematics and Mathematical Physics. 2020 Apr 1;60(4):580-589. doi: 10.1134/S0965542520040107

Author

Kabanikhin, S. I. ; Krivorotko, O. I. / Optimization Methods for Solving Inverse Immunology and Epidemiology Problems. In: Computational Mathematics and Mathematical Physics. 2020 ; Vol. 60, No. 4. pp. 580-589.

BibTeX

@article{8e99ac5023d7483f996f7acb49dec6a6,
title = "Optimization Methods for Solving Inverse Immunology and Epidemiology Problems",
abstract = "Inverse problems for systems of nonlinear ordinary differential equations are studied. In these problems, the unknown coefficients and initial data must be found given additional information about the solution to the corresponding direct problems; this information is obtained by measurements made at some specified points in time. Examples of inverse immunology and epidemiology problems arising in the analysis of infectious diseases progression, in the study of HIV dynamics, and spread of tuberculosis in highly endemic regions taking treatment into account are discussed. In the case when the solution to the inverse problem is not unique, three approaches to the study of identifiability of mathematical models are considered. A numerical solution algorithm based on the minimization of a quadratic objective functional is proposed. At the first stage, neighborhoods of the global minimizers are found, and gradient methods are used at the second stage. The gradient of the objective functional is calculated by solving the corresponding adjoint problem. Numerical results are discussed.",
keywords = "epidemiology, gradient method, gradient of functional, identification of parameters, immunology, inverse problems, ODE",
author = "Kabanikhin, {S. I.} and Krivorotko, {O. I.}",
year = "2020",
month = apr,
day = "1",
doi = "10.1134/S0965542520040107",
language = "English",
volume = "60",
pages = "580--589",
journal = "Computational Mathematics and Mathematical Physics",
issn = "0965-5425",
publisher = "PLEIADES PUBLISHING INC",
number = "4",

}

RIS

TY - JOUR

T1 - Optimization Methods for Solving Inverse Immunology and Epidemiology Problems

AU - Kabanikhin, S. I.

AU - Krivorotko, O. I.

PY - 2020/4/1

Y1 - 2020/4/1

N2 - Inverse problems for systems of nonlinear ordinary differential equations are studied. In these problems, the unknown coefficients and initial data must be found given additional information about the solution to the corresponding direct problems; this information is obtained by measurements made at some specified points in time. Examples of inverse immunology and epidemiology problems arising in the analysis of infectious diseases progression, in the study of HIV dynamics, and spread of tuberculosis in highly endemic regions taking treatment into account are discussed. In the case when the solution to the inverse problem is not unique, three approaches to the study of identifiability of mathematical models are considered. A numerical solution algorithm based on the minimization of a quadratic objective functional is proposed. At the first stage, neighborhoods of the global minimizers are found, and gradient methods are used at the second stage. The gradient of the objective functional is calculated by solving the corresponding adjoint problem. Numerical results are discussed.

AB - Inverse problems for systems of nonlinear ordinary differential equations are studied. In these problems, the unknown coefficients and initial data must be found given additional information about the solution to the corresponding direct problems; this information is obtained by measurements made at some specified points in time. Examples of inverse immunology and epidemiology problems arising in the analysis of infectious diseases progression, in the study of HIV dynamics, and spread of tuberculosis in highly endemic regions taking treatment into account are discussed. In the case when the solution to the inverse problem is not unique, three approaches to the study of identifiability of mathematical models are considered. A numerical solution algorithm based on the minimization of a quadratic objective functional is proposed. At the first stage, neighborhoods of the global minimizers are found, and gradient methods are used at the second stage. The gradient of the objective functional is calculated by solving the corresponding adjoint problem. Numerical results are discussed.

KW - epidemiology

KW - gradient method

KW - gradient of functional

KW - identification of parameters

KW - immunology

KW - inverse problems

KW - ODE

UR - http://www.scopus.com/inward/record.url?scp=85086226867&partnerID=8YFLogxK

U2 - 10.1134/S0965542520040107

DO - 10.1134/S0965542520040107

M3 - Article

AN - SCOPUS:85086226867

VL - 60

SP - 580

EP - 589

JO - Computational Mathematics and Mathematical Physics

JF - Computational Mathematics and Mathematical Physics

SN - 0965-5425

IS - 4

ER -

ID: 24516243