Optimizing Network Structure for Preventative Health

Diseases such as heart disease, stroke, or diabetes affect hundreds of millions of people. Such conditions are strongly impacted by obesity, and establishing healthy lifestyle behaviors is a critical public health challenge with many applications. Changing health behaviors is inherently a multiagent problem since people's behavior is strongly influenced by those around them. Hence, practitioners often attempt to modify the social network of a community by adding or removing edges in ways that will lead to desirable behavior change. To our knowledge, no previous work considers the algorithmic problem of finding the optimal set of edges to add and remove. We propose the RECONNECT algorithm, which efficiently finds high-quality solutions for a range of different network intervention problems. We evaluate RECONNECT in a highly realistic simulated environment based on the Antelope Valley region in California which draws on demographic, social, and health-related data. We find the RECONNECT outperforms an array of baseline policies, in some cases yielding a 150% improvement over the best alternative.