In a recent article that appeared in PLOS Computational Biology, a group of researchers were able to take what is essentially a model for the spread of infectious disease, and to apply it to one of our era’s most troubling social contagions, namely the spread of obesity. The authors of this study applied their infectious diseaese model to the epidemiological data recorded for the famous Framingham Heart Study1 . It is clear from this article that like many other natural phenomena that can be described mathematically, there is an essential and underlying commnonality in the properties of networks, whether they are the substrate for the transmission of physcial (or biological) content as is the case for infectious disease, or for behavioral traits of the kind highlighted in this article.
Here is the article authors’ own summary …
“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 per year and increases by 0.5 percentage points for each obese social contact, while the rate of recovering from obesity is 4 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.”
1 Dawber TR (1980) The Framingham study: the epidemiology of atherosclerotic disease. Cambridge: Harvard University Press
The author Gordon Webster, has spent his career working at the intersection of biology and computation and specializes in computational approaches to life science research and development.
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