Real-Time Environmental Trigger Modeling and Personalized Allergic Rhinitis Management in the United States: Exploring a Digital Twin Ecosystem

Abstract

Allergic rhinitis imposes a major health and socioeconomic burden. Real-time environmental trigger modeling and digital-twin technology promise a personalized approach to managing this burden. We review how a digital twin ecosystem can integrate live environmental data (such as pollen and pollution levels) with individual patient profiles (sensitization, symptoms, behaviors) to predict allergic rhinitis flares and inform tailored interventions. This narrative review synthesizes recent advances in digital architecture, data streams, and predictive analytics for allergic rhinitis. We discuss a layered digital-twin system that continuously fuses real-world exposures with personal health data to generate real-time risk assessments, treatment recommendations, and decision support. Early evidence suggests that such systems improve symptom tracking and enable preventive strategies to reduce flare-ups, but challenges remain in data integration, user engagement, and validation. We highlight clinical implications, cost benefits, technological gaps, and future directions for deploying digital twins in allergy care and broader public health initiatives worldwide.