Dynamic contagion potential framework for optimizing infection control in healthcare.

Introduction: Hospital-acquired infections (HAIs) caused by bacterial and viral pathogens continue to affect millions annually, placing a persistent burden on healthcare systems. Traditional infection control strategies often fall short due to their inability to assess real-time spatial and movement data within healthcare environments dynamically. This study addresses that gap by leveraging the concept of Methods: The proposed framework integrates CP, which encapsulates an individual's susceptibility and transmissibility, taking into account movement patterns and interactions across hospital units. Unlike models requiring precise tracking, this approach uses coarse location data to construct a dynamic infection risk landscape. CP parameters are continuously learned and updated over time through behavioral data, enabling real-time risk inference. The framework also introduces a CP-based optimization algorithm for patient-to-unit assignments that jointly minimizes contagion risk while satisfying clinical and logistical constraints. Results: The framework's efficacy is validated through modular and integrated evaluations. Simulations incorporate mobility patterns reflecting homogeneous and heterogeneous mixing, with infection spread following empirically grounded transmission models. Results demonstrate that incorporating CP significantly reduces infection propagation, enhances patient safety, and leads to more efficient healthcare resource allocation. Discussion: This study presents a dynamic, data-driven framework for infection control within healthcare facilities. By incorporating behavior-aware contagion metrics into patient flow decisions, the approach offers a scalable and proactive infection prevention strategy. The findings underscore the potential of CP to improve both operational outcomes and patient well-being in healthcare environments.