The reproduction number (R) and generation time are fundamental parameters in infectious disease epidemiology, guiding understanding of disease spread and informing outbreak response strategies. However, their estimation is complex due to the interdependence between R and generation times, and the challenges introduced by temporal changes in these parameters. These temporal changes can have multiple causes including changes in pathogen biology (e.g. new variants), changes in populationlevel behaviours (possibly prompted by interventions or public health messaging) and changes in the immune landscape in the population. Existing methods often fail to fully capture these dynamics, limiting their reliability for real-time decision-making during outbreaks.

This project aims to address these challenges by developing robust statistical methods for the joint estimation of R and generation times, with a focus on their dynamic interplay. This will include using household-level data from COVID-19 and other diseases in order to determine their utility for realtime decision making when analysed jointly with population-level data. It will also explore how temporal changes in one parameter affect the estimation of the other and what the implications are for outbreak modelling. The research will build on existing software tools for real-time estimation of reproduction numbers such as the EpiEstim and EpiNow2 R packages and ultimately aim to improve situational awareness in infectious disease outbreaks.

The project would suit anyone keen to get insights into the application of quantitative techniques in public health contexts, specifically advanced analytics applied in epidemiological contexts, as well as experience in inference with mathematical models applied to infectious disease data sets. It will be jointly supervised by Anne Cori (Imperial), Sebastian Funk (LSHTM) and Edwin van Leeuewen (UKHSA). Students are expected to have a postgraduate degree, ideally in a quantitative subject (e.g. Biostatistics, Bioinformatics, Mathematics, Statistics, Computer Science or Physics) or a related discipline (e.g. Epidemiology or Biology) with a strong quantitative element either awarded or imminent or equivalent training. At least some coding experience, ideally in R, is also required.