Enhancing Agent-Based Models with Real-Time Movement Data to Assess Impacts of Biosecurity Interventions on Disease Exposure in Healthcare Settings

Hospital-acquired infections (HAIs) pose a significant challenge in healthcare settings, contributing substantially to patient morbidity, mortality, and increased healthcare costs. HAI incidence arises from complex interactions among healthcare workers, patients, and contaminated medical equipment. Agent-based modeling (ABM) is a well-known tool for simulating HAI dynamics, but existing ABM solutions have limited efficacy because of their reliance on synthetic/random human movement data. To address this gap, we deploy an Ultra-Wideband (UWB) Real-Time Location System (RTLS) in the post-surgery observation unit of a tertiary hospital in our city and collect high-resolution spatiotemporal location data of healthcare workers and medical devices in that unit. We develop an agent-based model of HAI transmission that incorporates epidemiological parameters specific to Clostridioides difficile (C. diff), capturing both direct and indirect transmission routes. The model is calibrated using assimilated RTLS data and is then applied to forecast exposure risk associated with asymptomatic carriers in the presence of biosecurity interventions (i.e., hand hygiene and surface disinfection). Our simulation results, generated in the AnyLogic simulation software, demonstrate that exposure levels vary due to movement behavior and infection control measures. These findings highlight the necessity of integrating real-time location data into ABMs to enhance predictive accuracy and optimize intervention strategies.