This PhD opportunity is being offered as part of the LSTM and Lancaster University Doctoral Training Partnership. Find out more about the studentships and how to apply.
| Abstract | This project aims to explore routes of emergence and spread of novel variants of pandemic and globally endemic pathogens. A successful project will increase our understanding of where high risk locations for variant emergence are, and the routes by which such variants subsequently spread around through world. This insight will inform key locations for surveillance for the early detection of variants of concern, predict future trends of infection in globally endemic disease, and explore the use of interventions aimed at disrupting the cycle of infection driven by variant emergence. The project will be split into three parts, each of which would lead to a scientific manuscript: Part 1: Analysing patterns of pathogen emergence for influenza and SARS-CoV-2 in existing data. The first part of the project will seek to examine existing data collected in the GISAID database for influenza and SARS-CoV-2. This dataset comprises of a comprehensive list of viral genome sequences collected from across the globe. Spatial and temporal trends in genetic variation will be identified, to build a picture of evolution speed and spatial diversity. Trends will be compared between SARS-CoV-2 and influenza data to determine significant similarities and differences. Part 2: Development of a global model for pathogen evolution and spread. The second part of the project will focus on building a global infection model that incorporates national level population characteristics as well as data for human movement between countries. The model will include a dynamic for viral evolution, where increased diversity drives higher rates of reinfection. By fitting the model to the COVID and flu sequence data analysed in Part 1, the likely origins of significant viral variants will be identified, as well as the key routes of their subsequent global spread for both diseases. Part 3: Model exploration to aid pandemic preparedness. The final part of the project will be an exploration of the model dynamics constructed in Part 2. The aim will be to identify likely future infection trends (e.g., is SARS-CoV-2 likely to become seasonal like influenza, or is there a fundamental difference between the two?) and the effects of interventions aimed at disrupting the cycle of evolution driving infection, which in turn increases the probability of further evolution. |
| Where does this project lie in the translational pathway? | T2 - Human /Clinical Research,T3 - Evidence into Practice |
| Expected Outputs | The project will aim for a PhD by publication, where the expectation is that there would be at least 3 publications arising directly from the three parts of the project. Insight into the threat of viral evolution will be of significant interest to local public health agencies, wider scientific field, and international organisations such a WHO and BMGF. Outputs will be informative for both pandemic preparedness and mitigation of endemic disease and seasonal viral pathogens. |
| Training Opportunities | Training in statistical methods, programming for health data science, and building epidemiological models will be delivered through the core MSc modules, and optional MSc modules for infectious disease modelling at Lancaster University. Also, training in Bayesian model development and fitting will be provided at Lancaster and where available at IDDconf conference workshops or similar, and through links with other institutions as part of the JUNIPER consortium. |
| Skills Required | The student should be quantitatively orientated, with an aptitude for coding and mathematics/statistics. Experience in epidemiological modelling and)or data handling and/or would be strongly advantageous. |
|
Key Publications associated with this project |
Moore, Sam, et al. ""Retrospectively modeling the effects of increased global vaccine sharing on the COVID-19 pandemic."" Nature Medicine 28.11 (2022): 2416-2423. |
| Gangavarapu, Karthik, et al. ""Outbreak. info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations."" Nature Methods 20.4 (2023): 512-522. | |
| Read, Jonathan M., and Matt J. Keeling. ""Disease evolution on networks: the role of contact structure."" Proceedings of the Royal Society of London. Series B: Biological Sciences 270.1516 (2003): 699-708. |