Research
Conor’s research focuses on the application of advanced statistical and computational methods to tackle key challenges in healthcare and epidemiology. His work aims to improve the accuracy and reliability of disease forecasting models, using techniques like Bayesian inference and simple scoring rules to enhance public health predictions. Additionally, Conor employs simulation methods to optimize laboratory diagnostics, improving the efficiency and accuracy of identifying conditions like bacteremia. His research also extends to addressing the global threat of antimicrobial resistance by reviewing and applying Bayesian calibration methods to refine models that inform treatment strategies and antibiotic use. Through these interdisciplinary efforts, Conor seeks to bridge statistical modelling, healthcare, and public health to deliver actionable insights for improving patient outcomes and managing disease outbreaks.