Data-driven modeling of infections using real-time location data and electronic health records
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Hospital-acquired infections (HAIs) and respiratory disease outbreaks continue to pose significant challenges in indoor environments where the transmission dynamics are usually influenced by human movement patterns and contact tracing analytics. Traditional infection simulation models are based on synthetic movement assumptions and uses limited spatial representations which limits their capacity to analyze real-world disease transmission patterns. This dissertation addresses these limitations using data-driven modeling approach based on real-time movement and clinical data. The study outlines three enhancing disease/ infection modeling methodologies (i) mathematical modeling, (ii) agent-based simulation, and (iii) discrete-event simulation which are applied to two different contexts (i) academic indoor gatherings and (ii) hospital settings. Firstly, Ultra-Wideband (UWB) Real-Time Location System (RTLS) devices were deployed on four academic gatherings to capture second-level indoor movement data. These trajectories were used to model contact-driven respiratory disease spread dynamics in indoor settings through a mathematical modeling approach to calculate contact metrics, estimate transmission rates, and simulate basic reproduction numbers. Then, UWB RTLS devices were deployed in a post-surgery observation unit of an urban hospital to collect healthcare workers and mobile medical devices movement data. An agent-based model to simulate healthcare-associated infection transmission risk was developed to evaluate the impact of biosecurity measures such as mask use, hand hygiene, and device cleaning frequency, enabling systematic assessment of various intervention scenarios on infection spread dynamics. Further, electronic Health Record (EHR) data from an urban hospital were utilized to develop a discrete-event simulation model of Emergency Department patient flow, addressing the lack of operational understanding in infection and risk assessments. The model was developed to quantify how patient arrivals, service procedures, and the length of stay (LOS) affects the congestion and extended occupancy in specific ED areas. By modeling patient flow with observed clinical data the approach helps to identify where patients spend the most time and where crowding takes place thereby offering insight into delays and prolonged patient presence in certain ED areas. In summary, this dissertation demonstrates that integrating RTLS-derived movement data and EHR-based clinical data into modeling frameworks increases the accuracy, interpretability and practical relevance of disease models.
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Introduction -- Background work -- Mathematical modeling of respiratory diseases in indoor environments -- Agent-based modeling of HAI infection dynamics in clinical environments -- Patient flow modeling and simulation to study HAI incidents using electronic health records -- Conclusion
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Ph.D. (Doctor of Philosophy)