Discipline
Mathematical epidemiology; biostatistics; public health
Methodology
Compartmental modelling; state-space models; Bayesian inference; data augmentation; simulation and case-data analysis
Date
30 Jul 2026
Description
Case datasets often contain information on symptom onset, diagnosis, exposure, recovery and other events, yet these components are usually analysed separately before summary estimates are inserted into mechanistic transmission models. This project will develop an integrated framework that uses all available case-level information to estimate latent and infectious periods, reporting delays, transmission rates and reproduction numbers simultaneously. Statistical observation models will be linked directly to a mechanistic epidemic model, allowing uncertainty and dependence between epidemiological quantities to be retained throughout the analysis.
Keywords
Mechanistic models;joint inference;infectious period;case data
Would Suit Applicants Who
Have backgrounds in mathematics, statistics, data science, epidemiology or computer science and are interested in methodological research with public-health applications