Dueling biological and social contagions

Feng Fu, Nicholas A. Christakis, James H. Fowler
2017 Scientific Reports  
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
more » ... rmation about vaccination, flu, and face-to-face social networks. The results show that wellconnected 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 R 0 of 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. Voluntary mass vaccination is a fundamental strategy to achieve 'herd immunity' and to limit vaccine-preventable contagions 1-5 . However, a misalignment between individual self-interest and the public interest causes many people to remain unvaccinated 6-9 , which can expose the population to significant disease outbreaks and compromise efforts to eradicate the diseases in question. To understand vaccine compliance, considerable attention has been focused on integrating epidemiology with game-theoretic behavior models in recent years 10-23 . And while these models have devoted considerable attention to the incentives facing individuals deciding whether or not to vaccinate given the threat posed by the contagion in question, they have paid less attention to the social effect of those decisions on others. Yet, accumulating empirical data suggests that the phenomenon of social contagion is common 24-26 , especially when it comes to health decisions, such as smoking cessation 27 and vaccine adoption 28, 29 . Although a multitude of complex social factors may be involved 30 , the propagation of health behavior occurs when individual decisions are influenced by peers in social networks 10, 12, 14, 15, 18, 21, 22, 31 . That is, social phenomena may spread interpersonally in a manner similar (though not necessarily identical) 26,31 to biological contagion. And, in many public health settings, such social and biological contagions are often not independent 21 ; rather, they may interfere with each other, and may even be seen as dueling contagions. Indeed, a feedback loop can arise between the spread of an epidemic and the spread of health behaviours 20 . In response to perceived risks and social influence, individuals may take preventative measures such as vaccination 28 or reduced contact with others 13 , and these behavioural responses in turn modify the spread of infection 32-34 . It is, therefore, important to achieve a comprehensive understanding of the rich dynamics generated by this feedback process. Here, we reconstructed the temporal dynamics of concurrent spreading of vaccination behaviour and seasonal influenza in a real social network (Fig. 1) . The dataset has information regarding social network ties and individuals' health status during the 2009 H1N1 flu epidemic 35 . We note that data of this kind are both very scarce and also particularly well suited for the study of dueling contagions. Although it is common for epidemiological
doi:10.1038/srep43634 pmid:28252663 pmcid:PMC5333634 fatcat:q6ie6gws4nf7dnrtmsz3x3gu2a