"Research Fellow (Statistics)"

About the Project

Novel GM interventions for mosquito control could represent a step change in progressing towards elimination of the world’s most harmful vector-borne diseases, including malaria and dengue. However, conducting field trials of such interventions is challenging due to complex logistics and uncertain outcomes. We aim to develop novel methodological strategies that can reduce the resource requirements to rigorously evaluate new and uncertain community-level interventions in infectious disease control. A key component relates to harnessing advanced mechanistic disease transmission models to improve prediction of clinical trial outcomes (e.g. doi: 10.1038/s41467-024-53065-z). Given the uncertainty associated with intervention outcomes, we also aim to explore adaptive trial designs to enable studies to start small and refine their design as they progress. We will work closely with co-investigators from the Ifakara Health Institute (Tanzania) to ensure the methods are feasible and implementable in disease-endemic settings, and to improve impact. The project will involve a series of stakeholder engagement workshops, with opportunities for travel to Tanzania.

Role Summary

Work with the study investigators (Prof. Sam Watson (University of Birmingham), Dr Penelope Hancock (Imperial College London) and Prof. Fredros Okumu (University of Glasgow/Ifakara Health Institute) to develop and test new methods for trial design and analysis. Developing statistical models and code to run the proposed methods. Developing and running simulation-based analyses of the new methods.

Main Duties

The responsibilities may include some but not all of the responsibilities outlined below. Contribute to the development, documentation, and testing of new statistical methods. Develop code to run and evaluate statistical models, with the support of project supervisors. Support writing of research outputs for academic and lay audiences, and contribute to the development and running of stakeholder engagement activities in African countries. Contribute to writing bids for research funding. Apply knowledge in a way which develops new intellectual understanding. Disseminate research findings for publication, including research seminars and conferences. Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline.

Person Specification

PhD (or near to completion or equivalent experience) in a relevant field, including statistics, mathematics, computer science, epidemiology. Strong mathematical and quantitative skills. Experience in the implementation of mathematical or statistical models and model fitting, including Bayesian model fitting, is desirable but not essential. Familiarity or experience of management and analysis of large multidimensional real world data sets using Stata, R, Python, or similar. Knowledge of C++ would be advantageous but not essential. Ability to communicate complex information clearly. Informal enquiries to Samuel Watson, email: S.I.Watson@bham.ac.uk