Dynamic analysis and optimal control of a class of SISP respiratory diseases
In this paper, the actual background of the susceptible population being directly patients after inhaling a certain amount of PM is taken into account. The concentration response function of PM is introduced, and the SISP respiratory disease model is proposed. Qualitative theoretical analysis proves...
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| Veröffentlicht in: | Journal of biological dynamics 2022-12, Vol.16 (1), p.64-97 |
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| creator | Shi, Lei Qi, Longxing |
| description | In this paper, the actual background of the susceptible population being directly patients after inhaling a certain amount of PM
is taken into account. The concentration response function of PM
is introduced, and the SISP respiratory disease model is proposed. Qualitative theoretical analysis proves that the existence, local stability and global stability of the equilibria are all related to the daily emission
of PM
and PM
pathogenic threshold K. Based on the sensitivity factor analysis and time-varying sensitivity analysis of parameters on the number of patients, it is found that the conversion rate β and the inhalation rate η has the largest positive correlation. The cure rate γ of infected persons has the greatest negative correlation on the number of patients. The control strategy formulated by the analysis results of optimal control theory is as follows: The first step is to improve the clearance rate of PM
by reducing the PM
emissions and increasing the intensity of dust removal. Moreover, such removal work must be maintained for a long time. The second step is to improve the cure rate of patients by being treated in time. After that, people should be reminded to wear masks and go out less so as to reduce the conversion rate of susceptible people becoming patients. |
| doi_str_mv | 10.1080/17513758.2022.2027529 |
| format | Article |
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is taken into account. The concentration response function of PM
is introduced, and the SISP respiratory disease model is proposed. Qualitative theoretical analysis proves that the existence, local stability and global stability of the equilibria are all related to the daily emission
of PM
and PM
pathogenic threshold K. Based on the sensitivity factor analysis and time-varying sensitivity analysis of parameters on the number of patients, it is found that the conversion rate β and the inhalation rate η has the largest positive correlation. The cure rate γ of infected persons has the greatest negative correlation on the number of patients. The control strategy formulated by the analysis results of optimal control theory is as follows: The first step is to improve the clearance rate of PM
by reducing the PM
emissions and increasing the intensity of dust removal. Moreover, such removal work must be maintained for a long time. The second step is to improve the cure rate of patients by being treated in time. After that, people should be reminded to wear masks and go out less so as to reduce the conversion rate of susceptible people becoming patients.</description><identifier>ISSN: 1751-3758</identifier><identifier>EISSN: 1751-3766</identifier><identifier>DOI: 10.1080/17513758.2022.2027529</identifier><identifier>PMID: 35129084</identifier><language>eng</language><publisher>England: Taylor & Francis</publisher><subject>Air Pollutants - adverse effects ; Air Pollutants - analysis ; Control theory ; Emissions ; Environmental Monitoring - methods ; Factor analysis ; Humans ; Inhalation ; Models, Biological ; optimal control ; Particulate Matter - analysis ; pathogenic threshold ; pm $ _{2.5} $ emissions ; pm $ _{2.5} $ pathogenic threshold ; Respiratory disease ; Respiratory diseases ; Sensitivity analysis</subject><ispartof>Journal of biological dynamics, 2022-12, Vol.16 (1), p.64-97</ispartof><rights>2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2022</rights><rights>2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c437t-129f0154fc1cb6d02aff6190de67fdb9b86d40327ef38ea2f20ba0becf22c4d63</citedby><cites>FETCH-LOGICAL-c437t-129f0154fc1cb6d02aff6190de67fdb9b86d40327ef38ea2f20ba0becf22c4d63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/17513758.2022.2027529$$EPDF$$P50$$Ginformaworld$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/17513758.2022.2027529$$EHTML$$P50$$Ginformaworld$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2095,27481,27903,27904,59119,59120</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35129084$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shi, Lei</creatorcontrib><creatorcontrib>Qi, Longxing</creatorcontrib><title>Dynamic analysis and optimal control of a class of SISP respiratory diseases</title><title>Journal of biological dynamics</title><addtitle>J Biol Dyn</addtitle><description>In this paper, the actual background of the susceptible population being directly patients after inhaling a certain amount of PM
is taken into account. The concentration response function of PM
is introduced, and the SISP respiratory disease model is proposed. Qualitative theoretical analysis proves that the existence, local stability and global stability of the equilibria are all related to the daily emission
of PM
and PM
pathogenic threshold K. Based on the sensitivity factor analysis and time-varying sensitivity analysis of parameters on the number of patients, it is found that the conversion rate β and the inhalation rate η has the largest positive correlation. The cure rate γ of infected persons has the greatest negative correlation on the number of patients. The control strategy formulated by the analysis results of optimal control theory is as follows: The first step is to improve the clearance rate of PM
by reducing the PM
emissions and increasing the intensity of dust removal. Moreover, such removal work must be maintained for a long time. The second step is to improve the cure rate of patients by being treated in time. After that, people should be reminded to wear masks and go out less so as to reduce the conversion rate of susceptible people becoming patients.</description><subject>Air Pollutants - adverse effects</subject><subject>Air Pollutants - analysis</subject><subject>Control theory</subject><subject>Emissions</subject><subject>Environmental Monitoring - methods</subject><subject>Factor analysis</subject><subject>Humans</subject><subject>Inhalation</subject><subject>Models, Biological</subject><subject>optimal control</subject><subject>Particulate Matter - analysis</subject><subject>pathogenic threshold</subject><subject>pm $ _{2.5} $ emissions</subject><subject>pm $ _{2.5} $ pathogenic threshold</subject><subject>Respiratory disease</subject><subject>Respiratory diseases</subject><subject>Sensitivity analysis</subject><issn>1751-3758</issn><issn>1751-3766</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>0YH</sourceid><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><sourceid>DOA</sourceid><recordid>eNp9kU2LFDEQhoMo7rr6E5QGL15mzUcn6dyU9WtgQGH1HCpfkiHdGZMepP-9aWd2Dh6kICmKp94q6kXoJcG3BA_4LZGcMMmHW4opXR_JqXqErtf6hkkhHl9yPlyhZ7XuMeacSvEUXTFOqMJDf412H5YJxmg7mCAtNdaWuC4f5jhC6mye5pJTl0MHnU1Q65reb--_dcXXQyww57J0LlYP1dfn6EmAVP2L83-Dfnz6-P3uy2b39fP27v1uY3sm502bHTDhfbDEGuEwhRAEUdh5IYMzygzC9ZhR6QMbPNBAsQFsvA2U2t4JdoO2J12XYa8Ppe1aFp0h6r-FXH5qKHO0yWtrAgtmYARb0wvBlALsqWN-PQUNuGm9OWkdSv519HXWY6zWpwSTz8eqqWhBVYuGvv4H3edjaXdrlORiEFgx1Sh-omzJtRYfLgsSrFfr9IN1erVOn61rfa_O6kczenfpevCqAe9OQJxCLiP8ziU5PcOScgkFJhurZv-f8Qc_Dqbh</recordid><startdate>202212</startdate><enddate>202212</enddate><creator>Shi, Lei</creator><creator>Qi, Longxing</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><general>Taylor & Francis Group</general><scope>0YH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>8FD</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>DOA</scope></search><sort><creationdate>202212</creationdate><title>Dynamic analysis and optimal control of a class of SISP respiratory diseases</title><author>Shi, Lei ; Qi, Longxing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c437t-129f0154fc1cb6d02aff6190de67fdb9b86d40327ef38ea2f20ba0becf22c4d63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Air Pollutants - adverse effects</topic><topic>Air Pollutants - analysis</topic><topic>Control theory</topic><topic>Emissions</topic><topic>Environmental Monitoring - methods</topic><topic>Factor analysis</topic><topic>Humans</topic><topic>Inhalation</topic><topic>Models, Biological</topic><topic>optimal control</topic><topic>Particulate Matter - analysis</topic><topic>pathogenic threshold</topic><topic>pm $ _{2.5} $ emissions</topic><topic>pm $ _{2.5} $ pathogenic threshold</topic><topic>Respiratory disease</topic><topic>Respiratory diseases</topic><topic>Sensitivity analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shi, Lei</creatorcontrib><creatorcontrib>Qi, Longxing</creatorcontrib><collection>Taylor & Francis Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of biological dynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shi, Lei</au><au>Qi, Longxing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic analysis and optimal control of a class of SISP respiratory diseases</atitle><jtitle>Journal of biological dynamics</jtitle><addtitle>J Biol Dyn</addtitle><date>2022-12</date><risdate>2022</risdate><volume>16</volume><issue>1</issue><spage>64</spage><epage>97</epage><pages>64-97</pages><issn>1751-3758</issn><eissn>1751-3766</eissn><abstract>In this paper, the actual background of the susceptible population being directly patients after inhaling a certain amount of PM
is taken into account. The concentration response function of PM
is introduced, and the SISP respiratory disease model is proposed. Qualitative theoretical analysis proves that the existence, local stability and global stability of the equilibria are all related to the daily emission
of PM
and PM
pathogenic threshold K. Based on the sensitivity factor analysis and time-varying sensitivity analysis of parameters on the number of patients, it is found that the conversion rate β and the inhalation rate η has the largest positive correlation. The cure rate γ of infected persons has the greatest negative correlation on the number of patients. The control strategy formulated by the analysis results of optimal control theory is as follows: The first step is to improve the clearance rate of PM
by reducing the PM
emissions and increasing the intensity of dust removal. Moreover, such removal work must be maintained for a long time. The second step is to improve the cure rate of patients by being treated in time. After that, people should be reminded to wear masks and go out less so as to reduce the conversion rate of susceptible people becoming patients.</abstract><cop>England</cop><pub>Taylor & Francis</pub><pmid>35129084</pmid><doi>10.1080/17513758.2022.2027529</doi><tpages>34</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Air Pollutants - adverse effects Air Pollutants - analysis Control theory Emissions Environmental Monitoring - methods Factor analysis Humans Inhalation Models, Biological optimal control Particulate Matter - analysis pathogenic threshold pm $ _{2.5} $ emissions pm $ _{2.5} $ pathogenic threshold Respiratory disease Respiratory diseases Sensitivity analysis |
| title | Dynamic analysis and optimal control of a class of SISP respiratory diseases |
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