title: Dueling biological and social contagions.
abstract: Numerous models explore how a wide variety of biological and social phenomena spread in social networks. However, these models implicitly assume that the spread of one phenomenon is not affected by the spread of another. Here, we develop a model of "dueling contagions", with a particular illustration of a situation where one is biological (influenza) and the other is social (flu vaccination). We apply the model to unique time series data collected during the 2009 H1N1 epidemic that includes information about vaccination, flu, and face-to-face social networks. The results show that well-connected individuals are more likely to get vaccinated, as are people who are exposed to friends who get vaccinated or are exposed to friends who get the flu. Our dueling contagion model suggests that other epidemiological models may be dramatically underestimating the R0of contagions. It also suggests that the rate of vaccination contagion may be even more important than the biological contagion in determining the course of the disease. These results suggest that real world and online platforms that make it easier to see when friends have been vaccinated (personalized vaccination campaigns) and when they get the flu (personalized flu warnings) could have a large impact on reducing the severity of epidemics. They also suggest possible benefits from understanding the coevolution of many kinds of dueling contagions.
authors.affiliation: Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA.|Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA.
authors.forename: Feng
authors.initials: F
authors.lastname: Fu
authors.affiliation: Department of Medicine, Yale University, New Haven, Connecticut 06520, USA.|Department of Sociology, Yale University, New Haven, Connecticut 06520, USA.|Yale Institute of Network Science, Yale University, PO Box 208263, New Haven, Connecticut 06520, USA.
authors.forename: Nicholas A
authors.initials: NA
authors.lastname: Christakis
authors.affiliation: Division of Global Public Health, University of California, San Diego, La Jolla, CA 92093, USA.|Department of Political Science, University of California, San Diego, La Jolla, CA 92093, USA.
authors.forename: James H
authors.initials: JH
authors.lastname: Fowler
mesh_terms: D000465:Algorithms; D015438:Health Behavior; D007722:Health Knowledge, Attitudes, Practice; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000276:immunology; D007252:Influenza Vaccines; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control / Q000635:transmission; D008962:Models, Theoretical; D012919:Social Behavior; D012944:Social Support; D013334:Students; D011795:Surveys and Questionnaires; D014495:Universities; D014611:Vaccination
chemical_list: D007252:Influenza Vaccines