Modeling and Analysis of Group Dynamics in Alcohol-Consumption Environments

High-risk drinking is considered a major concern in public health, being the third leading preventable cause of death in the United States. Several studies have been conducted to understand the etiology of high-risk drinking and to design prevention strategies to reduce unhealthy alcohol-consumption and related problems, but there are still major gaps in identifying and investigating the key components that affect the consumption patterns during the drinking event. There is a need to develop tools for the design of methodologies to not only identify such dangerous patterns but also to determine how their dynamics impact the event. In this paper, based on current empirical evidence and observations of drinking events, we model a human group that is in an alcohol-consumption scenario as a dynamical system whose behavior is driven by the interplay between the environment, the network of interactions between the individuals, and their personal motivations and characteristics. We show how this mathematical model complements empirical research in this area by allowing us to analyze, simulate, and predict the drinking group behaviors, to improve the methodologies for field data collection, and to design interventions. Through simulations and Lyapunov stability theory, we provide a computational and mathematical analysis of the impact of the model parameters on the predicted dynamics of the drinking group at the drinking event level. Also, we show how the dynamical model can be informed using data collected in situ and to generate information that can complement the analysis.