Agent-based models of social network interventions promoting health and well-being: A systematic review

Social networks are complex adaptive systems that can profoundly influence health and well-being. Yet, conventional health interventions frequently isolate individuals without taking social networks into account. Social network interventions - which leverage social network characteristics to enhance intervention effectiveness - show promise but often lack robust design frameworks for impact assessment. Agent-based modeling (ABM) has emerged as a powerful computational tool for estimating social network effects and forecasting intervention outcomes across various scenarios. However, a comprehensive synthesis of ABM applications in social network interventions remains absent from the literature. This systematic review follows PRISMA-S guidelines to evaluate the implementation and effectiveness of social network interventions tested through agent-based models. We searched Scopus, Web of Science, and PubMed databases, identifying 1,282 initial papers, with 19 meeting inclusion criteria after screening and full-test assessment. Our analysis examines the types of simulated network interventions, their performance, and specific health contexts. This review will provide critical insights into the application of agent-based modeling in social network interventions, informing future research directions and intervention design in public health.