Show simple item record

dc.contributor.authorHill, Alison Lynn
dc.contributor.authorRand, David G.
dc.contributor.authorNowak, Martin A.
dc.contributor.authorChristakis, Nicholas A.
dc.date.accessioned2011-06-15T21:04:15Z
dc.date.available2011-06-15T21:04:15Z
dc.date.issued2010-11
dc.date.submitted2010-06
dc.identifier.issn1553-7358
dc.identifier.issn1553-734X
dc.identifier.urihttp://hdl.handle.net/1721.1/64447
dc.description.abstractMany behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for ‘automatic’ (or ‘spontaneous’) non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant R01GM078986)en_US
dc.description.sponsorshipNational Science Foundation (U.S.)en_US
dc.description.sponsorshipBill & Melinda Gates Foundationen_US
dc.description.sponsorshipTempleton Foundationen_US
dc.description.sponsorshipNational Institute on Aging (grant P01 AG031093)en_US
dc.description.sponsorshipFramingham Heart Study (contract number N01-HC-25195)en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pcbi.1000968en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/en_US
dc.sourcePLoSen_US
dc.titleInfectious Disease Modeling of Social Contagion in Networksen_US
dc.typeArticleen_US
dc.identifier.citationHill Alison Lynn, et al. "Infectious Disease Modeling of Social Contagion in Networks." PLoS Comput Biol 6(11): e1000968.en_US
dc.contributor.departmentWhitaker College of Health Sciences and Technologyen_US
dc.contributor.approverHill, Alison Lynn
dc.contributor.mitauthorHill, Alison Lynn
dc.relation.journalPloS Computational Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsHill, Alison L.; Rand, David G.; Nowak, Martin A.; Christakis, Nicholas A.en
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record