Infectious Disease Modeling of Social Contagion in Networks
PLoS Comput Biol, 6 (11):
e1000968 (November 2010)DOI: 10.1371/journal.pcbi.1000968
Abstract
<title>Author Summary</title>
<p>Information, trends, behaviors and even health states may spread between contacts in a social network, similar to disease transmission. However, a major difference is that as well as being spread infectiously, it is possible to acquire this state spontaneously. For example, you can gain knowledge of a particular piece of information either by being told about it, or by discovering it yourself. In this paper we introduce a mathematical modeling framework that allows us to compare the dynamics of these social contagions to traditional infectious diseases. We can also extract and compare the rates of spontaneous versus contagious acquisition of a behavior from longitudinal data and can use this to predict the implications for future prevalence and control strategies. As an example, we study the spread of obesity, and find that the current rate of becoming obese is about 2<inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="info:doi/10.1371/journal.pcbi.1000968.e004" mimetype="image" xlink:type="simple"/></inline-formula> per year and increases by 0.5 percentage points for each obese social contact, while the rate of recovering from obesity is 4<inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="info:doi/10.1371/journal.pcbi.1000968.e005" mimetype="image" xlink:type="simple"/></inline-formula> per year. The rates of spontaneous infection and transmission have steadily increased over time since 1970, driving the increase in obesity prevalence. Our model thus provides a quantitative way to analyze the strength and implications of social contagions.</p>
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