Discipline
Genomic epidemiology; infectious disease modelling; statistical methodology
Methodology
Stochastic modelling; Bayesian computation; simulation experiments; model comparison; analysis of pathogen genomic data
Date
30 Jul 2026
Description
Genomic surveillance datasets rarely represent a random sample of infections: sequencing effort changes over time, locations and population groups, and may increase during recognised outbreaks. This project will extend birth–death–sampling models to represent realistic sampling mechanisms and assess how incomplete or preferential sampling affects estimates of transmission dynamics. The work will use simulated and empirical datasets to compare alternative observation models and determine when explicitly modelling the sampling process materially improves inference.
Keywords
Genomic surveillance;preferential sampling;phylodynamics;observation models
Would Suit Applicants Who
Have quantitative training and an interest in Bayesian statistics, infectious disease epidemiology, population genetics or scientific computing