Understanding the impact of social networks on the spread of obesity

The spread of obesity through social networks has been well documented most notably by Christakis and Fowler in 2007. In this research we sought to understand the nature of the interaction between social networks, the spread of obesity and the behaviours that drive it. We applied this knowledge in a case study, seeking to evaluate the impact of these effects on different sub-groups of the population. These objectives were addressed in a hybrid systems modelling approach implemented in a hybrid simulation. An agent based model simulated the social network and embedded inside each agent was a system dynamics model replicating individual behaviour. The model was parameterised using a stochastic approximation algorithm. This approach allowed us to explore a range of scenarios and also evaluate the topology of the network generated by those scenarios. The model allowed us to forecast BMI (Body Mass Index) issues for different age-groups and genders. We were also able to infer the network topography and its effects. We found that for the youngest population sub-groups the network magnified the impact of external factors on the individuals weight, conversely for the other sub groups it acted to reduce that impact. The magnitude of the network effect was inversely correlated with age