No Cover Image

Journal article 404 views

The threshold of stochastic tumor-immune model with regime switching

Xing Chen, Xiaoyue Li, Yuting Ma, Chenggui Yuan Orcid Logo

Journal of Mathematical Analysis and Applications, Volume: 522, Issue: 1, Start page: 126956

Swansea University Author: Chenggui Yuan Orcid Logo

Full text not available from this repository: check for access using links below.

Check full text

DOI (Published version): 10.1016/j.jmaa.2022.126956

Abstract

In response to the pressing needs for comprehending the cancer biology, this paper focuses on dynamical behaviors of a class of stochastic tumor-immune models in random environment modulated by Markov chains. A sufficient and nearly necessary threshold-type criterion is investigated, which shows the...

Full description

Published in: Journal of Mathematical Analysis and Applications
ISSN: 0022-247X
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa62241
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2023-01-03T11:44:23Z
last_indexed 2023-02-04T04:13:25Z
id cronfa62241
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2023-02-03T12:08:36.5610403</datestamp><bib-version>v2</bib-version><id>62241</id><entry>2023-01-03</entry><title>The threshold of stochastic tumor-immune model with regime switching</title><swanseaauthors><author><sid>22b571d1cba717a58e526805bd9abea0</sid><ORCID>0000-0003-0486-5450</ORCID><firstname>Chenggui</firstname><surname>Yuan</surname><name>Chenggui Yuan</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-01-03</date><deptcode>SMA</deptcode><abstract>In response to the pressing needs for comprehending the cancer biology, this paper focuses on dynamical behaviors of a class of stochastic tumor-immune models in random environment modulated by Markov chains. A sufficient and nearly necessary threshold-type criterion is investigated, which shows the long-time behavior of the system can be classified by a real-value parameter &#x3BB;. Precisely, if , tumor cells die out. If , the system exists a unique invariant probability measure, and the transition probability of the solution process converges to this invariant measure. Moreover, we also estimate the expectations with respect to the invariant measure under some conditions. Two numerical examples are provided to illustrate our results.</abstract><type>Journal Article</type><journal>Journal of Mathematical Analysis and Applications</journal><volume>522</volume><journalNumber>1</journalNumber><paginationStart>126956</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0022-247X</issnPrint><issnElectronic/><keywords>Markov chain; Stochastic tumor-immune systems; Invariant measure; Ergodicity; Permanence; Extinction</keywords><publishedDay>1</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-06-01</publishedDate><doi>10.1016/j.jmaa.2022.126956</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SMA</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2023-02-03T12:08:36.5610403</lastEdited><Created>2023-01-03T11:42:39.0289688</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Mathematics</level></path><authors><author><firstname>Xing</firstname><surname>Chen</surname><order>1</order></author><author><firstname>Xiaoyue</firstname><surname>Li</surname><order>2</order></author><author><firstname>Yuting</firstname><surname>Ma</surname><order>3</order></author><author><firstname>Chenggui</firstname><surname>Yuan</surname><orcid>0000-0003-0486-5450</orcid><order>4</order></author></authors><documents><document><filename>Under embargo</filename><originalFilename>Under embargo</originalFilename><uploaded>2023-01-03T11:44:07.3145249</uploaded><type>Output</type><contentLength>3064840</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2023-12-28T00:00:00.0000000</embargoDate><documentNotes>&#xA9;2022 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND)</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by-nc-nd/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2023-02-03T12:08:36.5610403 v2 62241 2023-01-03 The threshold of stochastic tumor-immune model with regime switching 22b571d1cba717a58e526805bd9abea0 0000-0003-0486-5450 Chenggui Yuan Chenggui Yuan true false 2023-01-03 SMA In response to the pressing needs for comprehending the cancer biology, this paper focuses on dynamical behaviors of a class of stochastic tumor-immune models in random environment modulated by Markov chains. A sufficient and nearly necessary threshold-type criterion is investigated, which shows the long-time behavior of the system can be classified by a real-value parameter λ. Precisely, if , tumor cells die out. If , the system exists a unique invariant probability measure, and the transition probability of the solution process converges to this invariant measure. Moreover, we also estimate the expectations with respect to the invariant measure under some conditions. Two numerical examples are provided to illustrate our results. Journal Article Journal of Mathematical Analysis and Applications 522 1 126956 Elsevier BV 0022-247X Markov chain; Stochastic tumor-immune systems; Invariant measure; Ergodicity; Permanence; Extinction 1 6 2023 2023-06-01 10.1016/j.jmaa.2022.126956 COLLEGE NANME Mathematics COLLEGE CODE SMA Swansea University 2023-02-03T12:08:36.5610403 2023-01-03T11:42:39.0289688 Faculty of Science and Engineering School of Mathematics and Computer Science - Mathematics Xing Chen 1 Xiaoyue Li 2 Yuting Ma 3 Chenggui Yuan 0000-0003-0486-5450 4 Under embargo Under embargo 2023-01-03T11:44:07.3145249 Output 3064840 application/pdf Accepted Manuscript true 2023-12-28T00:00:00.0000000 ©2022 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title The threshold of stochastic tumor-immune model with regime switching
spellingShingle The threshold of stochastic tumor-immune model with regime switching
Chenggui Yuan
title_short The threshold of stochastic tumor-immune model with regime switching
title_full The threshold of stochastic tumor-immune model with regime switching
title_fullStr The threshold of stochastic tumor-immune model with regime switching
title_full_unstemmed The threshold of stochastic tumor-immune model with regime switching
title_sort The threshold of stochastic tumor-immune model with regime switching
author_id_str_mv 22b571d1cba717a58e526805bd9abea0
author_id_fullname_str_mv 22b571d1cba717a58e526805bd9abea0_***_Chenggui Yuan
author Chenggui Yuan
author2 Xing Chen
Xiaoyue Li
Yuting Ma
Chenggui Yuan
format Journal article
container_title Journal of Mathematical Analysis and Applications
container_volume 522
container_issue 1
container_start_page 126956
publishDate 2023
institution Swansea University
issn 0022-247X
doi_str_mv 10.1016/j.jmaa.2022.126956
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Mathematics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Mathematics
document_store_str 0
active_str 0
description In response to the pressing needs for comprehending the cancer biology, this paper focuses on dynamical behaviors of a class of stochastic tumor-immune models in random environment modulated by Markov chains. A sufficient and nearly necessary threshold-type criterion is investigated, which shows the long-time behavior of the system can be classified by a real-value parameter λ. Precisely, if , tumor cells die out. If , the system exists a unique invariant probability measure, and the transition probability of the solution process converges to this invariant measure. Moreover, we also estimate the expectations with respect to the invariant measure under some conditions. Two numerical examples are provided to illustrate our results.
published_date 2023-06-01T04:21:42Z
_version_ 1763754432539918336
score 11.016235

Similar Items