Improving tobacco social contagion models using agent-based simulations on networks

Tobacco use is the leading cause of preventable deaths in developed countries. Many interventions and policies have been implemented to reduce the levels of smoking but these policies rarely rely on models that capture the full complexity of the phenomenon. For instance, one feature usually neglected is the long-term effect of social contagion, although empirical research shows that this is a key driver of both tobacco initiation and cessation. One reason why social contagion is often dismissed is that existing models of smoking dynamics tend to be based on ordinary differential equation (ODE), which are not fit to study the impact of network effects on smoking dynamics. These models are also not flexible enough to consider all the interactions between individuals that may lead to initiation or cessation. To address this issue, we develop an agent-based model (ABM) that captures the complexity of social contagion in smoking dynamics. We validate our model with real-world data on historical prevalence of tobacco use in the US and UK. Importantly, our ABM follows empirical evidence and allows for both initiation and cessation to be either spontaneous or a consequence of social contagion. Additionally, we explore in detail the effect of the underlying network topology on smoking dynamics. We achieve this by testing our ABM on six different networks, both synthetic and real-world, including a fully-connected network to mimic ODE models. Our results suggest that a fully-connected network is not well-suited to replicate real data, highlighting the need for network models of smoking dynamics. Moreover, we show that when a real network is not available, good alternatives are networks generated by the Lancichinetti–Fortunato–Radicchi and Erdős–Rényi algorithms. Finally, we argue that, in light of these results, our ABM can be used to better study the long-term effects of tobacco control policies.