Jelicic, Nick; (2025) Aiding Infection Prevention and Control Decision-Making using mathematical and simulation-based modelling. Doctoral thesis (Ph.D), UCL (University College London).

Text Nick Jelicic Thesis - FINAL.pdf - Accepted Version Access restricted to UCL open access staff until 1 September 2026. Download (3MB)

Abstract

Hospital-acquired infections (HAIs) pose a significant risk to both staff and patients, potentially complicating patient treatment and causing serious illness that can increase morbidity to those infected and incur substantial costs for the NHS. Consequently, infection prevention and control (IPC) measures are often implemented to reduce transmission within the healthcare system. However, some of these measures can have a negative impact on patient flow through the system. This thesis focuses on the development of models to determine the complex con- sequences of IPC policies and help decision-makers to better assess the potential trade-offs between HAIs and patient flow. Through engagement with relevant stakeholders, a general description of scenarios where IPC policies have a potential impact on patient flow was developed, along with a set of use cases where potential trade-offs between infections and flow occur in practice. A modelling framework was formulated that, together with the problem description, contains all the necessary components to enable the development of models to quantify the consequences of IPC policies. This thesis presents analytical and simulation models to quantify these consequences. A Continuous-time Markov Chain analytical model is used to develop closed-form expressions for the model outputs. A Discrete Event Simulation model is also constructed, which is flexible to altering key model assumptions, enhancing its translatability across institutions. Each model is applied to an example scenario to demonstrate the outputs that the model can provide to assess the impact of different policies. The benefits of developing both models in parallel are discussed, including an analysis of the robustness of the analytical model to altering key model assumptions. Using these models, three methods are developed to aid decision-makers make more informed and reliable decisions. The benefits and limitations of each method are compared, with a particular focus on their likelihood of implementation across different hospitals.

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