A mathematical study of the COVID-19 propagation through a stochastic epidemic model

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dc.citation.epage795
dc.citation.issue3
dc.citation.journalTitleМатематичне моделювання та комп'ютинг
dc.citation.spage784
dc.contributor.affiliationУніверситет Сіді Мохамеда Бен Абделла
dc.contributor.affiliationSidi Mohamed Ben Abdellah University
dc.contributor.authorКіуах, Д.
dc.contributor.authorЕль-Ідріссі, С. Е. А.
dc.contributor.authorСаббар, Ю.
dc.contributor.authorKiouach, D.
dc.contributor.authorEl-Idrissi, S. E. A.
dc.contributor.authorSabbar, Y.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-03-04T12:17:25Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractCOVID-19 є великою небезпекою, яка загрожує всьому світу. У цьому контексті математичне моделювання є дуже потужним інструментом, щоб дізнатися більше про те, як така хвороба передається всередині людської популяції. У зв’язку з цим у цій статті пропонується стохастична модель епідемії, яка описує динаміку COVID-19 під час застосування карантину та стратегій медіа-висвітлення, і здійснено строгий математичний аналіз цієї моделі, щоб отримати загальне уявлення про поширення COVID-19.
dc.description.abstractThe COVID-19 is a major danger that threatens the whole world. In this context, mathematical modeling is a very powerful tool for knowing more about how such a disease is transmitted within a host population of humans. In this regard, we propose in the current study a stochastic epidemic model that describes the COVID-19 dynamics under the application of quarantine and coverage media strategies, and we give a rigorous mathematical analysis of this model to obtain an overview of COVID-19 dissemination behavior.
dc.format.extent784-795
dc.format.pages12
dc.identifier.citationKiouach D. A mathematical study of the COVID-19 propagation through a stochastic epidemic model / D. Kiouach, S. E. A. El-Idrissi, Y. Sabbar // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 10. — No 3. — P. 784–795.
dc.identifier.citationenKiouach D. A mathematical study of the COVID-19 propagation through a stochastic epidemic model / D. Kiouach, S. E. A. El-Idrissi, Y. Sabbar // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 10. — No 3. — P. 784–795.
dc.identifier.doidoi.org/10.23939/mmc2023.03.784
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/63514
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofМатематичне моделювання та комп'ютинг, 3 (10), 2023
dc.relation.ispartofMathematical Modeling and Computing, 3 (10), 2023
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dc.relation.referencesen[1] Jia J., Ding J., Liu S., Liao G., Li J., Duan B., Wang G., Zhang R. Modeling the control of COVID-19: Impact of policy interventions and meteorological factors. Preprint arXiv:2003.02985 (2020).
dc.relation.referencesen[2] Pawar D. D., Patil W. D., Raut D. K. Fractional-order mathematical model for analysing impact of quarantine on transmission of COVID-19 in India. Mathematical Modeling and Computing. 8 (2), 253–266 (2021).
dc.relation.referencesen[3] Ilnytskyi J. M. Modeling of the COVID-19 pandemic in the limit of no acquired immunity. Mathematical Modeling and Computing. 8 (2), 282–303 (2021).
dc.relation.referencesen[4] Yavorska O., Bun R. Spatial analysis of COVID-19 spread in Europe using "center of gravity" concept. Mathematical Modeling and Computing. 9 (1), 130–142 (2022).
dc.relation.referencesen[5] Kouidere A., Elhia M., Balatif O. A spatiotemporal spread of COVID-19 pandemic with vaccination optimal control strategy: A case study in Morocco. Mathematical Modeling and Computing. 10 (1), 171–185 (2023).
dc.relation.referencesen[6] Mao X. Stochastic differential equations and applications. Woodhead Publishing (2007).
dc.relation.referencesen[7] Karatzas I., Shreve S. E. Brownian Motion and Stochastic Calculus. Springer New York, NY (1998).
dc.relation.referencesen[8] Zhao Y., Jiang D. The threshold of a stochastic SIS epidemic model with vaccination. Applied Mathematics and Computation. 243, 718–727 (2014).
dc.relation.referencesen[9] Yin S. A New Generalization on Cauchy–Schwarz Inequality. Journal of Function Spaces. 2017, 9576375 (2017).
dc.relation.referencesen[10] Song Y., Miao A., Zhang T., Wang X., Liu J. Extinction and persistence of a stochastic SIRS epidemic model with saturated incidence rate and transfer from infectious to susceptible. Advances in Difference Equations. 2018, 293 (2018).
dc.relation.referencesen[11] Sun F. Dynamics of an imprecise stochastic Holling II one-predator two-prey system with jumps. Preprint arXiv:2006.14943 (2020).
dc.relation.referencesen[12] Nicholson J., Clapham C. The Concise Oxford Dictionary of Mathematics. Vol. 5. Oxford University Press Oxford (2014).
dc.relation.referencesen[13] Tang B., Wang X., Li Q., Bragazzi N. L., Tang S., Xiao Y., Wu J. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. Journal of Clinical Medicine. 9 (2), 462 (2020).
dc.relation.referencesen[14] Wu J., Tang B., Bragazzi N. L., Nah K., McCarthy Z. Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada. Journal of Mathematics in Industry. 10, 15 (2020).
dc.relation.referencesen[15] Public Health Ontario. Ontario COVID-19 Data Tool. PHO official website (2020).
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.subjectCOVID-19
dc.subjectброунівський рух
dc.subjectстохастична модель епідемії
dc.subjectвисвітлення ЗМІ
dc.subjectкарантин
dc.subjectCOVID-19
dc.subjectBrownian motion
dc.subjectstochastic epidemic model
dc.subjectcoverage media
dc.subjectquarantine
dc.titleA mathematical study of the COVID-19 propagation through a stochastic epidemic model
dc.title.alternativeМатематичне дослідження розповсюдження COVID-19 через стохастичну модель епідемії
dc.typeArticle

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Mathematical Modeling And Computing. – 2023. – Vol. 10, No. 3