Epidemiological delays, such as incubation periods, generation times, and reporting delays, are key to understanding infectious disease dynamics and informing outbreak responses. Despite advances, existing methods often struggle with biases, including incomplete data, and are limited in their ability to account for time-varying changes or to scale effectively for large datasets.

This project aims to address these challenges through methodological improvements and case studies on recent outbreaks of diseases such as COVID-19, mpox, and others. It will do so by identifying biases that could arise from common approaches and implications for modelbased predictions, as well as by assessing the impact of changes over time and epidemiological factors that could explain them. This project will involve refining existing statistical approaches to delay estimation and developing tools that enhance accessibility and scalability, building on efforts like the epidist R package, in order to improve critical decision-making during the initial stages of disease emergence.

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 Sebastian Funk (LSHTM), Anne Cori (Imperial) and Chris Overton (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.