The diffusion scattering parameters identification for a modified model of viral infection in the conditions of logistic dynamics of immunological cells

dc.citation.epage69
dc.citation.issue11
dc.citation.journalTitleМатематичне моделювання та комп'ютинг
dc.citation.spage59
dc.citation.volume1
dc.contributor.affiliationНаціональний університет водного господарства та природокористування
dc.contributor.affiliationNational University of Water and Environmental Engineering
dc.contributor.authorБарановський, С. В.
dc.contributor.authorБомба, А. Я.
dc.contributor.authorBaranovsky, S. V.
dc.contributor.authorBomba, A. Ya.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-10-20T07:44:27Z
dc.date.created2024-02-24
dc.date.issued2024-02-24
dc.description.abstractНа основі модифікації моделі інфекційного захворювання, що враховує дифузійні збурення та логістичну динаміку імунологічних клітин, запропоновано окремі підходи до ідентифікації параметрів дифузійного розсіювання для різних типів функціональної залежності коефіцієнтів дифузії та заданих умов перевизначення. Удосконалено спеціальну покрокову процедуру чисельної асимптотичної апроксимації розв'язку відповідної сингулярно збуреної модельної задачі із затримкою. Представлено результати комп'ютерних експериментів з ідентифікації невідомих параметрів дифузійного розсіювання. Зазначено, що ідентифікація та застосування змінних коефіцієнтів дифузії забезпечить точніше прогнозування динаміки інфекційного захворювання, що є важливим при прийнятті рішень щодо використання різних медичних процедур.
dc.description.abstractBased on the modification of the infectious disease model, taking into account diffusion disturbances and logistic dynamics of immunological cells, separate approaches to the diffusion scattering parameters identification for different types of functional dependence of diffusion coefficients and given redefinition conditions are proposed. A special step-by-step procedure for numerically asymptotic approximation of the solution to the corresponding singularly perturbed model problem with a delay has been improved. The results of computer experiments on identifying the unknown diffusion scattering parameters are presented. It is noted that the identification and application of variable diffusion coefficients will provide a more accurate prediction of the dynamics of an infectious disease, which is significant in decision-making regarding the use of various medical procedures.
dc.format.extent59-69
dc.format.pages11
dc.identifier.citationBaranovsky S. V. The diffusion scattering parameters identification for a modified model of viral infection in the conditions of logistic dynamics of immunological cells / S. V. Baranovsky, A. Ya. Bomba // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 1. — No 11. — P. 59–69.
dc.identifier.citationenBaranovsky S. V. The diffusion scattering parameters identification for a modified model of viral infection in the conditions of logistic dynamics of immunological cells / S. V. Baranovsky, A. Ya. Bomba // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 1. — No 11. — P. 59–69.
dc.identifier.doi10.23939/mmc2024.01.059
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/113798
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofМатематичне моделювання та комп'ютинг, 11 (1), 2024
dc.relation.ispartofMathematical Modeling and Computing, 11 (1), 2024
dc.relation.references[1] Marchuk G. I. Mathematical Modelling of Immune Response in Infectious Diseases. Dordrecht, Kluwer Press (1997).
dc.relation.references[2] Bocharov G., Volpert V., Ludewig B., Meyerhans A. Mathematical Immunology of Virus Infections. Springer, Cham (2018).
dc.relation.references[3] Martsenyuk V. P. Construction and study of stability of an antitumor immunity model. Cybernetics and Systems Analysis. 40 (5), 778–783 (2004).
dc.relation.references[4] Quintela B. M., Santos R. W., Lobosco M. On the coupling of two models of the human immune response to an antigen. BioMed Research International. 2014, 410457 (2014).
dc.relation.references[5] Chimal-Eguia J. C. Mathematical Model of Antiviral Immune Response against the COVID-19 Virus. Mathematics. 9 (12), 1356 (2021).
dc.relation.references[6] Bomba A., Baranovsky S., Pasichnyk M., Pryshchepa O. Modelling of the Infectious Disease Process with Taking into Account of Small-Scale Spatial-ly Distributed Influences. 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT 2020). 2, 62–65 (2020).
dc.relation.references[7] Bomba A., Baranovsky S., Pasichnyk M., Malash K. Modeling of Infectious Disease Dynamics under the Conditions of Spatial Perturbations and Taking into account Impulse Effects. Proceedings of the 3rd International Conference on Informatics and Data-Driven Medicine IDDM (2020). 2753, 119–128 (2020).
dc.relation.references[8] Baranovsky S. V., Bomba A. Ya., Lyashko S I. Generalization of the antiviral immune response model for complex consideration of diffusion perturbations, body temperature response, and logistic antigen population dynamics. Cybernetics and Systems Analysis. 58 (4), 576–592 (2022).
dc.relation.references[9] Bomba A., Baranovsky S., Blavatska O., Bachyshyna L. Infectious disease model generalization based on diffuse perturbations under conditions of body’s temperature reaction. Computers in Biology and Medicine. 146, 105561 (2022).
dc.relation.references[10] Ivanchov M. Inverse problems for equations of parabolic type. Math. Studies, Monograph Ser. Lviv, VNTL Publ. Vol. 10. (2003).
dc.relation.references[11] Bomba A. Ya., Safonyk A. P., Fursachyk O. A. Solving inverse singularly perturbed problems – mathematical models of filtering processes. Mathematical modeling. Dniprodzerzhynsk, DDTU. 1 (20), 62–65 (2009).
dc.relation.references[12] Soetaert K., Cash J. R., Mazzia F. Solving Differential Equations in R. Springer-Verlag Berlin and Heidelberg GmbH and Co. KG (2012).
dc.relation.references[13] Malachivskyy P. S., Melnychok L. S., Pizyur Ya. V. Chebyshev approximation of multivariable functions by the exponential expression. Cybernetics and Systems Analysis. 57 (3), 429–435 (2021).
dc.relation.references[14] Malachivskyy P. S., Pizyur Ya. V., Danchak N. V., Orazov E. B. Chebyshev approximation by exponential- power expression. Cybernetics and Systems Analysis. 49 (6), 877–881 (2013).
dc.relation.references[15] Vasil’eva A. B, Butuzov V. F., Nefedov N. N. Singularly perturbed problems with boundary and internal layers. Proceedings of the Steklov Institute of Mathematics. 268, 258–273 (2010).
dc.relation.references[16] Chernukha O., Chuchvara A. Modeling of the flows of admixtures in a random layered strip with probable arrangement of inclusions near the boundaries of the body. Journal of Mathematical Sciences. 238 (2), 116–128 (2019).
dc.relation.references[17] Chaplya Y., Chernukha O., Bilushchak Y. Mathematical modeling of the averaged concentration field in random stratified structures with regard for jumps of an unknown function on interfaces. of Mathematical Sciences. 225 (1), 62–74 (2018).
dc.relation.referencesen[1] Marchuk G. I. Mathematical Modelling of Immune Response in Infectious Diseases. Dordrecht, Kluwer Press (1997).
dc.relation.referencesen[2] Bocharov G., Volpert V., Ludewig B., Meyerhans A. Mathematical Immunology of Virus Infections. Springer, Cham (2018).
dc.relation.referencesen[3] Martsenyuk V. P. Construction and study of stability of an antitumor immunity model. Cybernetics and Systems Analysis. 40 (5), 778–783 (2004).
dc.relation.referencesen[4] Quintela B. M., Santos R. W., Lobosco M. On the coupling of two models of the human immune response to an antigen. BioMed Research International. 2014, 410457 (2014).
dc.relation.referencesen[5] Chimal-Eguia J. C. Mathematical Model of Antiviral Immune Response against the COVID-19 Virus. Mathematics. 9 (12), 1356 (2021).
dc.relation.referencesen[6] Bomba A., Baranovsky S., Pasichnyk M., Pryshchepa O. Modelling of the Infectious Disease Process with Taking into Account of Small-Scale Spatial-ly Distributed Influences. 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT 2020). 2, 62–65 (2020).
dc.relation.referencesen[7] Bomba A., Baranovsky S., Pasichnyk M., Malash K. Modeling of Infectious Disease Dynamics under the Conditions of Spatial Perturbations and Taking into account Impulse Effects. Proceedings of the 3rd International Conference on Informatics and Data-Driven Medicine IDDM (2020). 2753, 119–128 (2020).
dc.relation.referencesen[8] Baranovsky S. V., Bomba A. Ya., Lyashko S I. Generalization of the antiviral immune response model for complex consideration of diffusion perturbations, body temperature response, and logistic antigen population dynamics. Cybernetics and Systems Analysis. 58 (4), 576–592 (2022).
dc.relation.referencesen[9] Bomba A., Baranovsky S., Blavatska O., Bachyshyna L. Infectious disease model generalization based on diffuse perturbations under conditions of body’s temperature reaction. Computers in Biology and Medicine. 146, 105561 (2022).
dc.relation.referencesen[10] Ivanchov M. Inverse problems for equations of parabolic type. Math. Studies, Monograph Ser. Lviv, VNTL Publ. Vol. 10. (2003).
dc.relation.referencesen[11] Bomba A. Ya., Safonyk A. P., Fursachyk O. A. Solving inverse singularly perturbed problems – mathematical models of filtering processes. Mathematical modeling. Dniprodzerzhynsk, DDTU. 1 (20), 62–65 (2009).
dc.relation.referencesen[12] Soetaert K., Cash J. R., Mazzia F. Solving Differential Equations in R. Springer-Verlag Berlin and Heidelberg GmbH and Co. KG (2012).
dc.relation.referencesen[13] Malachivskyy P. S., Melnychok L. S., Pizyur Ya. V. Chebyshev approximation of multivariable functions by the exponential expression. Cybernetics and Systems Analysis. 57 (3), 429–435 (2021).
dc.relation.referencesen[14] Malachivskyy P. S., Pizyur Ya. V., Danchak N. V., Orazov E. B. Chebyshev approximation by exponential- power expression. Cybernetics and Systems Analysis. 49 (6), 877–881 (2013).
dc.relation.referencesen[15] Vasil’eva A. B, Butuzov V. F., Nefedov N. N. Singularly perturbed problems with boundary and internal layers. Proceedings of the Steklov Institute of Mathematics. 268, 258–273 (2010).
dc.relation.referencesen[16] Chernukha O., Chuchvara A. Modeling of the flows of admixtures in a random layered strip with probable arrangement of inclusions near the boundaries of the body. Journal of Mathematical Sciences. 238 (2), 116–128 (2019).
dc.relation.referencesen[17] Chaplya Y., Chernukha O., Bilushchak Y. Mathematical modeling of the averaged concentration field in random stratified structures with regard for jumps of an unknown function on interfaces. of Mathematical Sciences. 225 (1), 62–74 (2018).
dc.rights.holder© Національний університет “Львівська політехніка”, 2024
dc.subjectмодель інфекційного захворювання
dc.subjectідентифікація параметрів
dc.subjectдинамічні системи із запізненням
dc.subjectасимптотичні методи
dc.subjectсингулярно збурені задачі
dc.subjectлогістична динаміка
dc.subjectinfectious disease model
dc.subjectparameter identification
dc.subjectdynamic systems with delay
dc.subjectasymptotic method
dc.subjectsingularly perturbed problem
dc.subjectlogistic dynamics
dc.titleThe diffusion scattering parameters identification for a modified model of viral infection in the conditions of logistic dynamics of immunological cells
dc.title.alternativeІдентифікація параметрів дифузійного розсіювання модифікованої моделі вірусної інфекції в умовах логістичної динаміки імунологічних клітин
dc.typeArticle

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