Fanny Bergström: Computational Statistics for Infectious Disease Outbreaks

Abstract

Mathematical models of infectious diseases are important for informing public health decisions. Recent progress in computational techniques, along with increased access to surveillance data, has enabled more advanced modelling approaches. In particular, central to this thesis is the integration of Bayesian inference with mechanistic epidemic models to address problems such as reporting delays, intervention evaluation, and parameter uncertainty. This thesis aim of strengthen the theoretical and practical aspects of epidemic modelling for real-time monitoring, retrospective evaluation, and future preparedness.