Household members do not contact each other at random: implications for infectious disease modelling.
Identifieur interne : 000010 ( Main/Exploration ); précédent : 000009; suivant : 000011Household members do not contact each other at random: implications for infectious disease modelling.
Auteurs : Nele Goeyvaerts [Belgique] ; Eva Santermans [Belgique] ; Gail Potter [États-Unis] ; Andrea Torneri [Belgique] ; Kim Van Kerckhove [Belgique] ; Lander Willem [Belgique] ; Marc Aerts [Belgique] ; Philippe Beutels [Belgique] ; Niel Hens [Belgique]Source :
- Proceedings. Biological sciences [ 1471-2954 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
- Wicri :
- geographic : Belgique.
English descriptors
- KwdEn :
- MESH :
- geographic : Belgium.
- transmission : Communicable Diseases, Influenza, Human.
- Family Characteristics, Humans, Interpersonal Relations, Models, Theoretical, Social Networking.
Abstract
Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts, and thus easily spread within households. Epidemic models, used to gain insight into infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now, there has been no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) from 2010 to 2011. We analysed data from 318 households totalling 1266 individuals with household sizes ranging from two to seven members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between older siblings and between father and child are smaller than for any other pair. The epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulations. However, ignoring the contact density when inferring based on an epidemic model will result in biased estimates of within-household transmission rates. Further research regarding the implementation of within-household contact networks in epidemic models is necessary.
DOI: 10.1098/rspb.2018.2201
PubMed: 30963910
PubMed Central: PMC6304037
Affiliations:
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UHasselt , Hasselt </wicri:regionArea><wicri:noRegion>Hasselt </wicri:noRegion></affiliation></author><author><name sortKey="Santermans, Eva" sort="Santermans, Eva" uniqKey="Santermans E" first="Eva" last="Santermans">Eva Santermans</name><affiliation wicri:level="1"><nlm:affiliation>1 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt , Hasselt , Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>1 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt , Hasselt </wicri:regionArea><wicri:noRegion>Hasselt </wicri:noRegion></affiliation></author><author><name sortKey="Potter, Gail" sort="Potter, Gail" uniqKey="Potter G" first="Gail" last="Potter">Gail Potter</name><affiliation wicri:level="2"><nlm:affiliation>2 The Emmes Corporation , Rockville, MD , USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>2 The Emmes Corporation , Rockville, MD 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Statistical Bioinformatics, UHasselt , Hasselt , Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>1 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt , Hasselt </wicri:regionArea><wicri:noRegion>Hasselt </wicri:noRegion></affiliation></author><author><name sortKey="Willem, Lander" sort="Willem, Lander" uniqKey="Willem L" first="Lander" last="Willem">Lander Willem</name><affiliation wicri:level="1"><nlm:affiliation>3 Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp , Antwerp , Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>3 Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp , Antwerp </wicri:regionArea><wicri:noRegion>Antwerp </wicri:noRegion></affiliation></author><author><name sortKey="Aerts, Marc" sort="Aerts, Marc" uniqKey="Aerts M" first="Marc" last="Aerts">Marc Aerts</name><affiliation wicri:level="1"><nlm:affiliation>1 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt , Hasselt , Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>1 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt , Hasselt </wicri:regionArea><wicri:noRegion>Hasselt </wicri:noRegion></affiliation></author><author><name sortKey="Beutels, Philippe" sort="Beutels, Philippe" uniqKey="Beutels P" first="Philippe" last="Beutels">Philippe Beutels</name><affiliation wicri:level="1"><nlm:affiliation>3 Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp , Antwerp , Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>3 Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp , Antwerp </wicri:regionArea><wicri:noRegion>Antwerp </wicri:noRegion></affiliation></author><author><name sortKey="Hens, Niel" sort="Hens, Niel" uniqKey="Hens N" first="Niel" last="Hens">Niel Hens</name><affiliation wicri:level="1"><nlm:affiliation>1 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt , Hasselt , Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>1 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt , Hasselt </wicri:regionArea><wicri:noRegion>Hasselt </wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>3 Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp , Antwerp , Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>3 Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp , Antwerp </wicri:regionArea><wicri:noRegion>Antwerp </wicri:noRegion></affiliation></author></analytic><series><title level="j">Proceedings. Biological sciences</title><idno type="eISSN">1471-2954</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Belgium</term><term>Communicable Diseases (transmission)</term><term>Family Characteristics</term><term>Humans</term><term>Influenza, Human (transmission)</term><term>Interpersonal Relations</term><term>Models, Theoretical</term><term>Social Networking</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Belgique</term><term>Caractéristiques familiales</term><term>Grippe humaine (transmission)</term><term>Humains</term><term>Maladies transmissibles (transmission)</term><term>Modèles théoriques</term><term>Relations interpersonnelles</term><term>Réseautage social</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>Belgium</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Communicable Diseases</term><term>Influenza, Human</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Family Characteristics</term><term>Humans</term><term>Interpersonal Relations</term><term>Models, Theoretical</term><term>Social Networking</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Belgique</term><term>Caractéristiques familiales</term><term>Grippe humaine</term><term>Humains</term><term>Maladies transmissibles</term><term>Modèles théoriques</term><term>Relations interpersonnelles</term><term>Réseautage social</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Belgique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts, and thus easily spread within households. Epidemic models, used to gain insight into infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now, there has been no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) from 2010 to 2011. We analysed data from 318 households totalling 1266 individuals with household sizes ranging from two to seven members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between older siblings and between father and child are smaller than for any other pair. The epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulations. However, ignoring the contact density when inferring based on an epidemic model will result in biased estimates of within-household transmission rates. Further research regarding the implementation of within-household contact networks in epidemic models is necessary.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30963910</PMID><DateCompleted><Year>2019</Year><Month>09</Month><Day>05</Day></DateCompleted><DateRevised><Year>2020</Year><Month>03</Month><Day>09</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1471-2954</ISSN><JournalIssue CitedMedium="Internet"><Volume>285</Volume><Issue>1893</Issue><PubDate><Year>2018</Year><Month>Dec</Month><Day>19</Day></PubDate></JournalIssue><Title>Proceedings. Biological sciences</Title><ISOAbbreviation>Proc. Biol. Sci.</ISOAbbreviation></Journal><ArticleTitle>Household members do not contact each other at random: implications for infectious disease modelling.</ArticleTitle><Pagination><MedlinePgn>20182201</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1098/rspb.2018.2201</ELocationID><Abstract><AbstractText>Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts, and thus easily spread within households. Epidemic models, used to gain insight into infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now, there has been no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) from 2010 to 2011. We analysed data from 318 households totalling 1266 individuals with household sizes ranging from two to seven members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between older siblings and between father and child are smaller than for any other pair. The epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulations. However, ignoring the contact density when inferring based on an epidemic model will result in biased estimates of within-household transmission rates. Further research regarding the implementation of within-household contact networks in epidemic models is necessary.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Goeyvaerts</LastName><ForeName>Nele</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>1 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt , Hasselt , Belgium.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Santermans</LastName><ForeName>Eva</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>1 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt , Hasselt , Belgium.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Potter</LastName><ForeName>Gail</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>2 The Emmes Corporation , Rockville, MD , USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Torneri</LastName><ForeName>Andrea</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>3 Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp , Antwerp , Belgium.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Van Kerckhove</LastName><ForeName>Kim</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>1 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt , Hasselt , Belgium.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Willem</LastName><ForeName>Lander</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>3 Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp , Antwerp , Belgium.</Affiliation></AffiliationInfo></Author><Author 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Hens</name><name sortKey="Santermans, Eva" sort="Santermans, Eva" uniqKey="Santermans E" first="Eva" last="Santermans">Eva Santermans</name><name sortKey="Torneri, Andrea" sort="Torneri, Andrea" uniqKey="Torneri A" first="Andrea" last="Torneri">Andrea Torneri</name><name sortKey="Van Kerckhove, Kim" sort="Van Kerckhove, Kim" uniqKey="Van Kerckhove K" first="Kim" last="Van Kerckhove">Kim Van Kerckhove</name><name sortKey="Willem, Lander" sort="Willem, Lander" uniqKey="Willem L" first="Lander" last="Willem">Lander Willem</name></country><country name="États-Unis"><region name="Maryland"><name sortKey="Potter, Gail" sort="Potter, Gail" uniqKey="Potter G" first="Gail" last="Potter">Gail Potter</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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