Estimating infectious disease parameters from data on social contacts and serological status
In dynamic models of infectious disease transmission, typically various mixing patterns are imposed on the so-called 'who acquires infection from whom' matrix. These imposed mixing patterns are based on prior knowledge of age-related social mixing behaviour rather than observations. Altern...
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description | In dynamic models of infectious disease transmission, typically various mixing patterns are imposed on the so-called 'who acquires infection from whom' matrix. These imposed mixing patterns are based on prior knowledge of age-related social mixing behaviour rather than observations. Alternatively, we can assume that transmission rates for infections transmitted predominantly through non-sexual social contacts are proportional to rates of conversational contact which can be estimated from a contact survey. In general, however, contacts reported in social contact surveys are proxies of those events by which transmission may occur and there may be age-specific characteristics that are related to susceptibility and infectiousness which are not captured by the contact rates. Therefore, we model transmission as the product of two age-specific variables: the age-specific contact rate and an age-specific proportionality factor, which entails an improvement of fit for the seroprevalence of the varicella zoster virus in Belgium. Furthermore, we address the effect on the estimation of the basic reproduction number, using non-parametric bootstrapping to account for different sources of variability and using multimodel inference to deal with model selection uncertainty. The method proposed makes it possible to obtain important information on transmission dynamics that cannot be inferred from approaches that have been traditionally applied hitherto. |
doi_str_mv | 10.1111/j.1467-9876.2009.00693.x |
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These imposed mixing patterns are based on prior knowledge of age-related social mixing behaviour rather than observations. Alternatively, we can assume that transmission rates for infections transmitted predominantly through non-sexual social contacts are proportional to rates of conversational contact which can be estimated from a contact survey. In general, however, contacts reported in social contact surveys are proxies of those events by which transmission may occur and there may be age-specific characteristics that are related to susceptibility and infectiousness which are not captured by the contact rates. Therefore, we model transmission as the product of two age-specific variables: the age-specific contact rate and an age-specific proportionality factor, which entails an improvement of fit for the seroprevalence of the varicella zoster virus in Belgium. Furthermore, we address the effect on the estimation of the basic reproduction number, using non-parametric bootstrapping to account for different sources of variability and using multimodel inference to deal with model selection uncertainty. The method proposed makes it possible to obtain important information on transmission dynamics that cannot be inferred from approaches that have been traditionally applied hitherto.</description><identifier>ISSN: 0035-9254</identifier><identifier>EISSN: 1467-9876</identifier><identifier>DOI: 10.1111/j.1467-9876.2009.00693.x</identifier><identifier>CODEN: APSTAG</identifier><language>eng</language><publisher>Oxford, UK: Oxford, UK : Blackwell Publishing Ltd</publisher><subject>Age ; Age structure ; Applications ; Basic reproduction number ; Belgium ; Bootstrap method ; Bootstrap procedure ; Chicken pox ; Confidence interval ; Contact potentials ; Data transmission ; Disease transmission ; Diseases ; Epidemiology ; Estimating techniques ; Estimation methods ; Exact sciences and technology ; Global analysis, analysis on manifolds ; Infections ; Infectious diseases ; Mathematics ; Medical research ; Model averageing ; Model selection ; Modeling ; Parametric inference ; Parametric models ; Probability and statistics ; Sciences and techniques of general use ; Serology ; Social contact data ; Statistical methods ; Statistics ; Studies ; Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds ; Transmission mechanism ; Transmission parameters ; Varicella-zoster virus ; Who acquires infection from whom matrix</subject><ispartof>Applied statistics, 2010-03, Vol.59 (2), p.255-277</ispartof><rights>Copyright 2010 The Royal Statistical Society and Blackwell Publishing Ltd.</rights><rights>2010 Royal Statistical Society</rights><rights>2015 INIST-CNRS</rights><rights>2010 The Royal Statistical Society and Blackwell Publishing Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c7333-fd50883d8ee4248d96fe8b9101d8cc613278f338e920e5fb71299aeda355f8133</citedby><cites>FETCH-LOGICAL-c7333-fd50883d8ee4248d96fe8b9101d8cc613278f338e920e5fb71299aeda355f8133</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/40541685$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/40541685$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,832,1417,4008,27924,27925,45574,45575,58017,58021,58250,58254</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22393855$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttp://econpapers.repec.org/article/blajorssc/v_3a59_3ay_3a2010_3ai_3a2_3ap_3a255-277.htm$$DView record in RePEc$$Hfree_for_read</backlink></links><search><creatorcontrib>Goeyvaerts, Nele</creatorcontrib><creatorcontrib>Hens, Niel</creatorcontrib><creatorcontrib>Ogunjimi, Benson</creatorcontrib><creatorcontrib>Aerts, Marc</creatorcontrib><creatorcontrib>Shkedy, Ziv</creatorcontrib><creatorcontrib>Damme, Pierre Van</creatorcontrib><creatorcontrib>Beutels, Philippe</creatorcontrib><title>Estimating infectious disease parameters from data on social contacts and serological status</title><title>Applied statistics</title><description>In dynamic models of infectious disease transmission, typically various mixing patterns are imposed on the so-called 'who acquires infection from whom' matrix. These imposed mixing patterns are based on prior knowledge of age-related social mixing behaviour rather than observations. Alternatively, we can assume that transmission rates for infections transmitted predominantly through non-sexual social contacts are proportional to rates of conversational contact which can be estimated from a contact survey. In general, however, contacts reported in social contact surveys are proxies of those events by which transmission may occur and there may be age-specific characteristics that are related to susceptibility and infectiousness which are not captured by the contact rates. Therefore, we model transmission as the product of two age-specific variables: the age-specific contact rate and an age-specific proportionality factor, which entails an improvement of fit for the seroprevalence of the varicella zoster virus in Belgium. Furthermore, we address the effect on the estimation of the basic reproduction number, using non-parametric bootstrapping to account for different sources of variability and using multimodel inference to deal with model selection uncertainty. The method proposed makes it possible to obtain important information on transmission dynamics that cannot be inferred from approaches that have been traditionally applied hitherto.</description><subject>Age</subject><subject>Age structure</subject><subject>Applications</subject><subject>Basic reproduction number</subject><subject>Belgium</subject><subject>Bootstrap method</subject><subject>Bootstrap procedure</subject><subject>Chicken pox</subject><subject>Confidence interval</subject><subject>Contact potentials</subject><subject>Data transmission</subject><subject>Disease transmission</subject><subject>Diseases</subject><subject>Epidemiology</subject><subject>Estimating techniques</subject><subject>Estimation methods</subject><subject>Exact sciences and technology</subject><subject>Global analysis, analysis on manifolds</subject><subject>Infections</subject><subject>Infectious diseases</subject><subject>Mathematics</subject><subject>Medical research</subject><subject>Model averageing</subject><subject>Model selection</subject><subject>Modeling</subject><subject>Parametric inference</subject><subject>Parametric models</subject><subject>Probability and statistics</subject><subject>Sciences and techniques of general use</subject><subject>Serology</subject><subject>Social contact data</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Studies</subject><subject>Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds</subject><subject>Transmission mechanism</subject><subject>Transmission parameters</subject><subject>Varicella-zoster virus</subject><subject>Who acquires infection from whom matrix</subject><issn>0035-9254</issn><issn>1467-9876</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNqNkU1vEzEQhlcIJELhJyBWSIjTBn-u7QMHFLXloxSpacUFyXK83uBls049DiT_Hi9b5cApluyxNM87nvFbFCVGc5zXu26OWS0qJUU9JwipOUK1ovP9o2J2TDwuZghRXinC2dPiGUCH8sKIzYof55D8xiQ_rEs_tM4mH3ZQNh6cAVduTTQbl1yEso1hUzYmmTIMJQTrTV_aMCRjE5RmaEpwMfRh7W1OQDJpB8-LJ63pwb14iGfF3cX57eJjdfXt8tPiw1VlBaW0ahuOpKSNdI4RJhtVt06uFEa4kdbWmBIhW0qlUwQ53q4EJkoZ1xjKeSsxpWfF26nuNob7nYOkNx6s63szuDyNFoxxSRhFJ5BUKIYJPoVkQiFKMvn6P7ILuzjkgTVBuFaKoxGSE2RjAIiu1duY_z0eNEZ6NFJ3evRLj37p0Uj9z0i9z9LPkzS6rbNH3ao3XYgAVv_W1HCVj0Pe-UmUgx-veW_HyLkmQuifaZOLvXlo1kA2qo1msB6ORQmhikrOM_d-4v743h1OblbfLJeLfMv6l5O-gxTiUc8QZ7iWY_1qyntIbn_Mm_hL14IKrr9fX-rl7dfFl4ubaz3a8WriWxO0Wcfc890yz0oRlohgROlf8Czrdw</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Goeyvaerts, Nele</creator><creator>Hens, Niel</creator><creator>Ogunjimi, Benson</creator><creator>Aerts, Marc</creator><creator>Shkedy, Ziv</creator><creator>Damme, Pierre Van</creator><creator>Beutels, Philippe</creator><general>Oxford, UK : Blackwell Publishing Ltd</general><general>Blackwell Publishing Ltd</general><general>Wiley-Blackwell</general><general>Royal Statistical Society</general><general>Oxford University Press</general><scope>FBQ</scope><scope>BSCLL</scope><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8BJ</scope><scope>8FD</scope><scope>FQK</scope><scope>JBE</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7U9</scope><scope>H94</scope></search><sort><creationdate>201003</creationdate><title>Estimating infectious disease parameters from data on social contacts and serological status</title><author>Goeyvaerts, Nele ; Hens, Niel ; Ogunjimi, Benson ; Aerts, Marc ; Shkedy, Ziv ; Damme, Pierre Van ; Beutels, Philippe</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c7333-fd50883d8ee4248d96fe8b9101d8cc613278f338e920e5fb71299aeda355f8133</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Age</topic><topic>Age structure</topic><topic>Applications</topic><topic>Basic reproduction number</topic><topic>Belgium</topic><topic>Bootstrap method</topic><topic>Bootstrap procedure</topic><topic>Chicken pox</topic><topic>Confidence interval</topic><topic>Contact potentials</topic><topic>Data transmission</topic><topic>Disease transmission</topic><topic>Diseases</topic><topic>Epidemiology</topic><topic>Estimating techniques</topic><topic>Estimation methods</topic><topic>Exact sciences and technology</topic><topic>Global analysis, analysis on manifolds</topic><topic>Infections</topic><topic>Infectious diseases</topic><topic>Mathematics</topic><topic>Medical research</topic><topic>Model averageing</topic><topic>Model selection</topic><topic>Modeling</topic><topic>Parametric inference</topic><topic>Parametric models</topic><topic>Probability and statistics</topic><topic>Sciences and techniques of general use</topic><topic>Serology</topic><topic>Social contact data</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Studies</topic><topic>Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds</topic><topic>Transmission mechanism</topic><topic>Transmission parameters</topic><topic>Varicella-zoster virus</topic><topic>Who acquires infection from whom matrix</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Goeyvaerts, Nele</creatorcontrib><creatorcontrib>Hens, Niel</creatorcontrib><creatorcontrib>Ogunjimi, Benson</creatorcontrib><creatorcontrib>Aerts, Marc</creatorcontrib><creatorcontrib>Shkedy, Ziv</creatorcontrib><creatorcontrib>Damme, Pierre Van</creatorcontrib><creatorcontrib>Beutels, Philippe</creatorcontrib><collection>AGRIS</collection><collection>Istex</collection><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Virology and AIDS Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><jtitle>Applied statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Goeyvaerts, Nele</au><au>Hens, Niel</au><au>Ogunjimi, Benson</au><au>Aerts, Marc</au><au>Shkedy, Ziv</au><au>Damme, Pierre Van</au><au>Beutels, Philippe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating infectious disease parameters from data on social contacts and serological status</atitle><jtitle>Applied statistics</jtitle><date>2010-03</date><risdate>2010</risdate><volume>59</volume><issue>2</issue><spage>255</spage><epage>277</epage><pages>255-277</pages><issn>0035-9254</issn><eissn>1467-9876</eissn><coden>APSTAG</coden><abstract>In dynamic models of infectious disease transmission, typically various mixing patterns are imposed on the so-called 'who acquires infection from whom' matrix. These imposed mixing patterns are based on prior knowledge of age-related social mixing behaviour rather than observations. Alternatively, we can assume that transmission rates for infections transmitted predominantly through non-sexual social contacts are proportional to rates of conversational contact which can be estimated from a contact survey. In general, however, contacts reported in social contact surveys are proxies of those events by which transmission may occur and there may be age-specific characteristics that are related to susceptibility and infectiousness which are not captured by the contact rates. Therefore, we model transmission as the product of two age-specific variables: the age-specific contact rate and an age-specific proportionality factor, which entails an improvement of fit for the seroprevalence of the varicella zoster virus in Belgium. Furthermore, we address the effect on the estimation of the basic reproduction number, using non-parametric bootstrapping to account for different sources of variability and using multimodel inference to deal with model selection uncertainty. The method proposed makes it possible to obtain important information on transmission dynamics that cannot be inferred from approaches that have been traditionally applied hitherto.</abstract><cop>Oxford, UK</cop><pub>Oxford, UK : Blackwell Publishing Ltd</pub><doi>10.1111/j.1467-9876.2009.00693.x</doi><tpages>23</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Age Age structure Applications Basic reproduction number Belgium Bootstrap method Bootstrap procedure Chicken pox Confidence interval Contact potentials Data transmission Disease transmission Diseases Epidemiology Estimating techniques Estimation methods Exact sciences and technology Global analysis, analysis on manifolds Infections Infectious diseases Mathematics Medical research Model averageing Model selection Modeling Parametric inference Parametric models Probability and statistics Sciences and techniques of general use Serology Social contact data Statistical methods Statistics Studies Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds Transmission mechanism Transmission parameters Varicella-zoster virus Who acquires infection from whom matrix |
title | Estimating infectious disease parameters from data on social contacts and serological status |
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