Health behavior homophily can mitigate the spread of infectious diseases in small-world networks
Research has repeatedly shown that the spread of infectious diseases is influenced by properties of our social networks. Small-world like structures with densely connected clusters bridged by only a few connections, for example, are not only known to diminish disease spread, but also to increase the...
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description | Research has repeatedly shown that the spread of infectious diseases is influenced by properties of our social networks. Small-world like structures with densely connected clusters bridged by only a few connections, for example, are not only known to diminish disease spread, but also to increase the chance for a disease to spread to any part of the network. Clusters composed of individuals who show similar reactions to avoid infections (health behavior homophily), however, might change the effect of such clusters on disease spread. To study the combined effect of health behavior homophily and small-world network properties on disease spread, we extend a previously developed ego-centered network formation model and agent-based simulation. Based on more than 80,000 simulated epidemics on generated networks varying in clustering and homophily, as well as diseases varying in severity and infectivity, we predict that the existence of health behavior homophilous clusters reduce the number of infections, lower peak size, and flatten the curve of active cases. That is because agents perceiving higher risks of infections can protect their cluster from infections comparatively quickly by severing only a few bridging ties. A comparison with epidemics in static network structures shows that the incapability to act upon risk perceptions and the low connectivity between clusters in static networks lead to diametrically opposed effects with comparatively large epidemics and prolonged epidemics. These finding suggest that micro-level behavioral adaptation to health risks mitigate macro-level disease spread to an extent that is not captured by static network models of disease spread. Furthermore, this mechanism can be used to design information campaigns targeting proxies for groups with lower risk perception.
•Disease spread in small-world networks among health behavior heterogeneous agents.•Epidemics are significantly smaller and shorter in adaptive than in static networks.•Homophily on health behavior reduces epidemics in adaptive networks.•Homophily effects are especially pronounced in clustered networks.•Proxies for health behaviors may be used for targeted intervention campaigns. |
doi_str_mv | 10.1016/j.socscimed.2022.115350 |
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•Disease spread in small-world networks among health behavior heterogeneous agents.•Epidemics are significantly smaller and shorter in adaptive than in static networks.•Homophily on health behavior reduces epidemics in adaptive networks.•Homophily effects are especially pronounced in clustered networks.•Proxies for health behaviors may be used for targeted intervention campaigns.</description><identifier>ISSN: 0277-9536</identifier><identifier>EISSN: 1873-5347</identifier><identifier>DOI: 10.1016/j.socscimed.2022.115350</identifier><identifier>PMID: 36183539</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Adaptive networks ; Agent-based simulations ; Cluster Analysis ; Communicable Diseases - epidemiology ; Epidemics ; Epidemics - prevention & control ; Health Behavior ; Homophily ; Humans ; Infectious diseases ; Risk perception ; Small-world networks ; Social Networking</subject><ispartof>Social science & medicine (1982), 2022-11, Vol.312, p.115350-115350, Article 115350</ispartof><rights>2022 The Authors</rights><rights>Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c420t-cb78230028b7aab35b6c4cccb85e0d89f52756bc297489fe6ffa70a818575e623</citedby><cites>FETCH-LOGICAL-c420t-cb78230028b7aab35b6c4cccb85e0d89f52756bc297489fe6ffa70a818575e623</cites><orcidid>0000-0002-4483-7238 ; 0000-0002-4083-3651</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.socscimed.2022.115350$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36183539$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Nunner, Hendrik</creatorcontrib><creatorcontrib>Buskens, Vincent</creatorcontrib><creatorcontrib>Teslya, Alexandra</creatorcontrib><creatorcontrib>Kretzschmar, Mirjam</creatorcontrib><title>Health behavior homophily can mitigate the spread of infectious diseases in small-world networks</title><title>Social science & medicine (1982)</title><addtitle>Soc Sci Med</addtitle><description>Research has repeatedly shown that the spread of infectious diseases is influenced by properties of our social networks. Small-world like structures with densely connected clusters bridged by only a few connections, for example, are not only known to diminish disease spread, but also to increase the chance for a disease to spread to any part of the network. Clusters composed of individuals who show similar reactions to avoid infections (health behavior homophily), however, might change the effect of such clusters on disease spread. To study the combined effect of health behavior homophily and small-world network properties on disease spread, we extend a previously developed ego-centered network formation model and agent-based simulation. Based on more than 80,000 simulated epidemics on generated networks varying in clustering and homophily, as well as diseases varying in severity and infectivity, we predict that the existence of health behavior homophilous clusters reduce the number of infections, lower peak size, and flatten the curve of active cases. That is because agents perceiving higher risks of infections can protect their cluster from infections comparatively quickly by severing only a few bridging ties. A comparison with epidemics in static network structures shows that the incapability to act upon risk perceptions and the low connectivity between clusters in static networks lead to diametrically opposed effects with comparatively large epidemics and prolonged epidemics. These finding suggest that micro-level behavioral adaptation to health risks mitigate macro-level disease spread to an extent that is not captured by static network models of disease spread. Furthermore, this mechanism can be used to design information campaigns targeting proxies for groups with lower risk perception.
•Disease spread in small-world networks among health behavior heterogeneous agents.•Epidemics are significantly smaller and shorter in adaptive than in static networks.•Homophily on health behavior reduces epidemics in adaptive networks.•Homophily effects are especially pronounced in clustered networks.•Proxies for health behaviors may be used for targeted intervention campaigns.</description><subject>Adaptive networks</subject><subject>Agent-based simulations</subject><subject>Cluster Analysis</subject><subject>Communicable Diseases - epidemiology</subject><subject>Epidemics</subject><subject>Epidemics - prevention & control</subject><subject>Health Behavior</subject><subject>Homophily</subject><subject>Humans</subject><subject>Infectious diseases</subject><subject>Risk perception</subject><subject>Small-world networks</subject><subject>Social Networking</subject><issn>0277-9536</issn><issn>1873-5347</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkMlOwzAQhi0EoqXwCuAjlxQvcewcK8QmVeICZ-M4E-KSxMVOQX17XLVw5TTbP9uH0BUlc0pocbOaR2-jdT3Uc0YYm1MquCBHaEqV5JnguTxGU8KkzErBiwk6i3FFCKFE8VM04QVVXPByit4ewXRjiytozZfzAbe-9-vWdVtszYB7N7p3MwIeW8BxHcDU2DfYDQ3Y0flNxLWLYCLElMOxN12XffvQ1XiAMTkf8RydNKaLcHGwM_R6f_dy-5gtnx-ebhfLzOaMjJmtpGKcEKYqaUzFRVXY3FpbKQGkVmUjmBRFZVkp8xRB0TRGEqOoElJAwfgMXe_nroP_3EAcde-iha4zA6Q7NZOMlKws8p1U7qU2-BgDNHodXG_CVlOid3j1Sv_h1Tu8eo83dV4elmyqXe2375dnEiz2AkivfjkIOk2BwULtQiKma-_-XfID4OuRLw</recordid><startdate>202211</startdate><enddate>202211</enddate><creator>Nunner, Hendrik</creator><creator>Buskens, Vincent</creator><creator>Teslya, Alexandra</creator><creator>Kretzschmar, Mirjam</creator><general>Elsevier Ltd</general><scope>6I.</scope><scope>AAFTH</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>7X8</scope><orcidid>https://orcid.org/0000-0002-4483-7238</orcidid><orcidid>https://orcid.org/0000-0002-4083-3651</orcidid></search><sort><creationdate>202211</creationdate><title>Health behavior homophily can mitigate the spread of infectious diseases in small-world networks</title><author>Nunner, Hendrik ; Buskens, Vincent ; Teslya, Alexandra ; Kretzschmar, Mirjam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-cb78230028b7aab35b6c4cccb85e0d89f52756bc297489fe6ffa70a818575e623</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adaptive networks</topic><topic>Agent-based simulations</topic><topic>Cluster Analysis</topic><topic>Communicable Diseases - epidemiology</topic><topic>Epidemics</topic><topic>Epidemics - prevention & control</topic><topic>Health Behavior</topic><topic>Homophily</topic><topic>Humans</topic><topic>Infectious diseases</topic><topic>Risk perception</topic><topic>Small-world networks</topic><topic>Social Networking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nunner, Hendrik</creatorcontrib><creatorcontrib>Buskens, Vincent</creatorcontrib><creatorcontrib>Teslya, Alexandra</creatorcontrib><creatorcontrib>Kretzschmar, Mirjam</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect: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>MEDLINE - Academic</collection><jtitle>Social science & medicine (1982)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nunner, Hendrik</au><au>Buskens, Vincent</au><au>Teslya, Alexandra</au><au>Kretzschmar, Mirjam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Health behavior homophily can mitigate the spread of infectious diseases in small-world networks</atitle><jtitle>Social science & medicine (1982)</jtitle><addtitle>Soc Sci Med</addtitle><date>2022-11</date><risdate>2022</risdate><volume>312</volume><spage>115350</spage><epage>115350</epage><pages>115350-115350</pages><artnum>115350</artnum><issn>0277-9536</issn><eissn>1873-5347</eissn><abstract>Research has repeatedly shown that the spread of infectious diseases is influenced by properties of our social networks. Small-world like structures with densely connected clusters bridged by only a few connections, for example, are not only known to diminish disease spread, but also to increase the chance for a disease to spread to any part of the network. Clusters composed of individuals who show similar reactions to avoid infections (health behavior homophily), however, might change the effect of such clusters on disease spread. To study the combined effect of health behavior homophily and small-world network properties on disease spread, we extend a previously developed ego-centered network formation model and agent-based simulation. Based on more than 80,000 simulated epidemics on generated networks varying in clustering and homophily, as well as diseases varying in severity and infectivity, we predict that the existence of health behavior homophilous clusters reduce the number of infections, lower peak size, and flatten the curve of active cases. That is because agents perceiving higher risks of infections can protect their cluster from infections comparatively quickly by severing only a few bridging ties. A comparison with epidemics in static network structures shows that the incapability to act upon risk perceptions and the low connectivity between clusters in static networks lead to diametrically opposed effects with comparatively large epidemics and prolonged epidemics. These finding suggest that micro-level behavioral adaptation to health risks mitigate macro-level disease spread to an extent that is not captured by static network models of disease spread. Furthermore, this mechanism can be used to design information campaigns targeting proxies for groups with lower risk perception.
•Disease spread in small-world networks among health behavior heterogeneous agents.•Epidemics are significantly smaller and shorter in adaptive than in static networks.•Homophily on health behavior reduces epidemics in adaptive networks.•Homophily effects are especially pronounced in clustered networks.•Proxies for health behaviors may be used for targeted intervention campaigns.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>36183539</pmid><doi>10.1016/j.socscimed.2022.115350</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-4483-7238</orcidid><orcidid>https://orcid.org/0000-0002-4083-3651</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adaptive networks Agent-based simulations Cluster Analysis Communicable Diseases - epidemiology Epidemics Epidemics - prevention & control Health Behavior Homophily Humans Infectious diseases Risk perception Small-world networks Social Networking |
title | Health behavior homophily can mitigate the spread of infectious diseases in small-world networks |
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