Discipline
Infectious disease modelling; biostatistics; genomic epidemiology
Methodology
Mathematical modelling; Bayesian inference; simulation studies; identifiability analysis; computational statistics
Date
30 Jul 2026
Description
Birth–death–sampling models are widely used to infer transmission and population dynamics from pathogen sequence data, but their estimates can be sensitive to assumptions about sampling and observation processes. This project will investigate when key epidemiological parameters—such as transmission, removal and sampling rates—can be reliably estimated, identify common sources of bias or non-identifiability, and develop practical guidance for fitting these models to real outbreak data.
Keywords
Phylodynamics;birth–death models;parameter identifiability;Bayesian inference
Would Suit Applicants Who
Have interests in mathematical modelling, statistics, epidemiology or computational biology, and are comfortable working with R, Julia or a similar programming language