Modelling the impact of precaution on disease dynamics and its evolution
In this paper, we introduce the notion of practically susceptible population , which is a fraction of the biologically susceptible population. Assuming that the fraction depends on the severity of the epidemic and the public’s level of precaution (as a response of the public to the epidemic), we pro...
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description | In this paper, we introduce the notion of
practically susceptible population
, which is a fraction of the
biologically susceptible
population. Assuming that the fraction depends on the severity of the epidemic and the public’s level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease’s eventual vanishing for the framework model under the assumption that the basic reproduction number
R
0
<
1
. For
R
0
>
1
, we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the
instantaneous best response
function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the
delayed best response
, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing “adopting the best response” with “adapting toward the best response”, we also explore the adaptive long-term dynamics. |
doi_str_mv | 10.1007/s00285-024-02100-0 |
format | Article |
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practically susceptible population
, which is a fraction of the
biologically susceptible
population. Assuming that the fraction depends on the severity of the epidemic and the public’s level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease’s eventual vanishing for the framework model under the assumption that the basic reproduction number
R
0
<
1
. For
R
0
>
1
, we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the
instantaneous best response
function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the
delayed best response
, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing “adopting the best response” with “adapting toward the best response”, we also explore the adaptive long-term dynamics.</description><identifier>ISSN: 0303-6812</identifier><identifier>ISSN: 1432-1416</identifier><identifier>EISSN: 1432-1416</identifier><identifier>DOI: 10.1007/s00285-024-02100-0</identifier><identifier>PMID: 38709376</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applications of Mathematics ; Basic Reproduction Number - statistics & numerical data ; Biological Evolution ; Communicable Diseases - epidemiology ; Communicable Diseases - transmission ; Computer Simulation ; Convergence ; Disease Susceptibility - epidemiology ; Epidemics ; Epidemics - prevention & control ; Epidemics - statistics & numerical data ; Epidemiological Models ; Evolution ; Humans ; Mathematical and Computational Biology ; Mathematical Concepts ; Mathematics ; Mathematics and Statistics ; Models, Biological ; Response functions</subject><ispartof>Journal of mathematical biology, 2024-07, Vol.89 (1), p.1, Article 1</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c326t-a450d024babf471ef23589177c38a9aad45ed9ac936a5a8d4ec41531feed11c23</cites><orcidid>0000-0002-8403-3314</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00285-024-02100-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00285-024-02100-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38709376$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cheng, Tianyu</creatorcontrib><creatorcontrib>Zou, Xingfu</creatorcontrib><title>Modelling the impact of precaution on disease dynamics and its evolution</title><title>Journal of mathematical biology</title><addtitle>J. Math. Biol</addtitle><addtitle>J Math Biol</addtitle><description>In this paper, we introduce the notion of
practically susceptible population
, which is a fraction of the
biologically susceptible
population. Assuming that the fraction depends on the severity of the epidemic and the public’s level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease’s eventual vanishing for the framework model under the assumption that the basic reproduction number
R
0
<
1
. For
R
0
>
1
, we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the
instantaneous best response
function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the
delayed best response
, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing “adopting the best response” with “adapting toward the best response”, we also explore the adaptive long-term dynamics.</description><subject>Applications of Mathematics</subject><subject>Basic Reproduction Number - statistics & numerical data</subject><subject>Biological Evolution</subject><subject>Communicable Diseases - epidemiology</subject><subject>Communicable Diseases - transmission</subject><subject>Computer Simulation</subject><subject>Convergence</subject><subject>Disease Susceptibility - epidemiology</subject><subject>Epidemics</subject><subject>Epidemics - prevention & control</subject><subject>Epidemics - statistics & numerical data</subject><subject>Epidemiological Models</subject><subject>Evolution</subject><subject>Humans</subject><subject>Mathematical and Computational Biology</subject><subject>Mathematical Concepts</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Models, Biological</subject><subject>Response functions</subject><issn>0303-6812</issn><issn>1432-1416</issn><issn>1432-1416</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE9LAzEQxYMotla_gAcJePGymslk_x2lqBUUL3oOaTJbt3R362ZX6Lc37VYFD0KGQOY3b14eY-cgrkGI9MYLIbM4ElKFCi-ROGBjUCgjUJAcsrFAgVGSgRyxE--XQkAa53DMRpilIsc0GbPZc-NotSrrBe_eiZfV2tiONwVft2RN35VNzcNxpSfjibtNbarSem5qx8vOc_psVjvqlB0VZuXpbH9P2Nv93et0Fj29PDxOb58iizLpIqNi4YLjuZkXKgUqJMZZDmlqMTO5MU7F5HJjc0xMbDKnyCqIEQoiB2AlTtjVoLtum4-efKer0tvwBVNT03uNIgYlE8QtevkHXTZ9Wwd3O0oiJAoDJQfKto33LRV63ZaVaTcahN7mrIecdXCtdzlrEYYu9tL9vCL3M_IdbABwAHxo1Qtqf3f_I_sFM5eHdg</recordid><startdate>20240701</startdate><enddate>20240701</enddate><creator>Cheng, Tianyu</creator><creator>Zou, Xingfu</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><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>7TK</scope><scope>7TM</scope><scope>7U9</scope><scope>8FD</scope><scope>FR3</scope><scope>H94</scope><scope>JQ2</scope><scope>K9.</scope><scope>M7N</scope><scope>M7Z</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-8403-3314</orcidid></search><sort><creationdate>20240701</creationdate><title>Modelling the impact of precaution on disease dynamics and its evolution</title><author>Cheng, Tianyu ; Zou, Xingfu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c326t-a450d024babf471ef23589177c38a9aad45ed9ac936a5a8d4ec41531feed11c23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Applications of Mathematics</topic><topic>Basic Reproduction Number - statistics & numerical data</topic><topic>Biological Evolution</topic><topic>Communicable Diseases - epidemiology</topic><topic>Communicable Diseases - transmission</topic><topic>Computer Simulation</topic><topic>Convergence</topic><topic>Disease Susceptibility - epidemiology</topic><topic>Epidemics</topic><topic>Epidemics - prevention & control</topic><topic>Epidemics - statistics & numerical data</topic><topic>Epidemiological Models</topic><topic>Evolution</topic><topic>Humans</topic><topic>Mathematical and Computational Biology</topic><topic>Mathematical Concepts</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Models, Biological</topic><topic>Response functions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cheng, Tianyu</creatorcontrib><creatorcontrib>Zou, Xingfu</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biochemistry Abstracts 1</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of mathematical biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cheng, Tianyu</au><au>Zou, Xingfu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modelling the impact of precaution on disease dynamics and its evolution</atitle><jtitle>Journal of mathematical biology</jtitle><stitle>J. Math. Biol</stitle><addtitle>J Math Biol</addtitle><date>2024-07-01</date><risdate>2024</risdate><volume>89</volume><issue>1</issue><spage>1</spage><pages>1-</pages><artnum>1</artnum><issn>0303-6812</issn><issn>1432-1416</issn><eissn>1432-1416</eissn><abstract>In this paper, we introduce the notion of
practically susceptible population
, which is a fraction of the
biologically susceptible
population. Assuming that the fraction depends on the severity of the epidemic and the public’s level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease’s eventual vanishing for the framework model under the assumption that the basic reproduction number
R
0
<
1
. For
R
0
>
1
, we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the
instantaneous best response
function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the
delayed best response
, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing “adopting the best response” with “adapting toward the best response”, we also explore the adaptive long-term dynamics.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>38709376</pmid><doi>10.1007/s00285-024-02100-0</doi><orcidid>https://orcid.org/0000-0002-8403-3314</orcidid></addata></record> |
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subjects | Applications of Mathematics Basic Reproduction Number - statistics & numerical data Biological Evolution Communicable Diseases - epidemiology Communicable Diseases - transmission Computer Simulation Convergence Disease Susceptibility - epidemiology Epidemics Epidemics - prevention & control Epidemics - statistics & numerical data Epidemiological Models Evolution Humans Mathematical and Computational Biology Mathematical Concepts Mathematics Mathematics and Statistics Models, Biological Response functions |
title | Modelling the impact of precaution on disease dynamics and its evolution |
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