Sequential Stochastic Network Structure Optimization With Applications To Addressing Canada's Obesity Epidemic

Abstract

Canada is currently experiencing an obesity epidemic that threatens the overall quality of life of the population by increasing the risk of diabetes and cardiovascular disease, among other health conditions. Obesity is a health condition that is significantly influenced by lifestyle choices which are often influenced directly by the lifestyle choices of individuals in a person's immediate environment through a social learning process. Community level preventative health interventions are often regarded as a viable route to curb the prevalence of obesity. The modification of a community's social network by the introduction of mentorship pairings is a powerful tool for preventative health interventions. In this work, we introduce a novel mathematical network model for preventative health interventions that allows for dynamic modification of the network. We develop algorithms to approximately solve this novel formulation at large scale and we rigorously explore their theoretical properties. We create a realistic simulation environment for interventions occurring in the region of Montreal, Canada, and use this environment to empirically evaluate the performance of the algorithms we develop. We find that our algorithms significantly outperform all baseline interventions. Moreover, for fixed computational resources, our algorithms can address problems of significantly greater size than the best existing alternative algorithm.