Highly pathogenic avian influenza viruses (HPAIV) present a significant threat to public health, food security, and the poultry industry. The viral, population, environmental and socio-economic factors directly or indirectly driving pathogenic evolution, spread patterns, and epidemic prevalence across different transmission interfaces require investigation, especially for most recently evolved 2022/2023 strains which are showing a much-enhanced host range.
With the aim to develop a comprehensive monitoring framework combining different data sources, the project will improve understanding of the evolution and spread of currently circulating avian influenza virus of H5 subtype as an exemplar. This project will seek to enhance risk prediction frameworks for zoonotic diseases, provide guidance for prevention and control strategies, and contribute to global public health.
This project benefits from extensive datasets comprising avian influenza virus sequences and surveillance data from collaborations on both national and international scales, and will seek to incorporate relevant open data at suitable spatial temporal scales to enhance model robustness and utility.
Outline activities of this project could include:
1) Quantify the evolution, transmission, and spread patterns of H5 HPAIVs in cross-species transmission interfaces.
2) Identify the drivers influencing HPAIV spread and cross-species transmission.
3) Investigate the impact of environmental factors, land use, biodiversity, human behavior, and vaccination policies on HPAIV spread and emergence.
In this project the student will receive interdisciplinary training in bioinformatics, phylodynamic modelling and machine learning to analyze the global impact of climate change on the spread and emergence of zoonotic pathogens. Key aspects are likely to include:
1) Incorporating environmental factors as key determinants in predicting disease risk, considering climate and land use changes.
2) Integrating mathematical algorithms and infectious disease phylodynamic models to improve disease prediction and understand transmission dynamics.
3) Leveraging data-driven innovations, such as data transformation, sharing, and visualization, to enhance understanding and communication of research outcomes.
Funding information and application procedures:
This 3.5 year studentship opportunity is open to UK and international students and provides funding to cover stipend, tuition fees and consumable/travel costs.
Application form can be downloaded via https://www.ed.ac.uk/sites/default/files/atoms/files/rdsvs_gaffs_roslin_foundation_studentship_application_form_2024-25.doc
Please send your completed Application Form to [Email Address Removed]
If you are applying for more than one studentship please submit a separate application with a closing date of noon on 8th January 2024 at https://www.ed.ac.uk/roslin/work-study/opportunities/studentships