Tracking the evolution, spread and key drivers of zoonotic diseases

Project offered for Ker Memorial PhD Studentship in Infectious Diseases

Highly pathogenic avian influenza viruses (HPAIV) present a significant threat to public health, food security, and the poultry industry. With the potential to infect humans and recent evidence of increased virulence and wider geographical spread, understanding the drivers of HPAIV evolution and spread is crucial. This project is about tracking HPAIV emergence and spread through different countries using population scale data on humans and animals, and virus sequence data. Recent outbreaks of HPAIV subtype H5 in sea lions and farmed minks raise concerns about the virus evolving efficient mammalian transmission and the possibility of a future pandemic. However, 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 scores, the project will improve understanding of the evolution and spread of currently circulating avian influenza virus as an exemplar, including cross-species transmissions and underlying influential drivers. By extending the study to other pathogens of interest, which are influenced by similar population, environmental and/or socio-economic drivers, insights into zoonotic disease emergence will also be gained. 

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. Developing a statistical model to track the evolution, transmission, and spread patterns of HPAIVs in cross-species transmission interfaces.
2. Use phylodynamic and statistical modeling, along with machine learning methods, to 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.

Overall 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.

1. In this project the student will have the opportunity to employ advanced phylodynamic modeling techniques, cutting-edge statistical modeling tools, and machine learning methods to analyze the global impact of climate change on the spread and emergence of zoonotic pathogens. Key aspects are likely to include:
2. Incorporating environmental factors as key determinants in predicting disease risk, considering climate and land use changes;
3. Integrating mathematical algorithms and infectious disease phylodynamic models to improve disease prediction and understand transmission dynamics;
4. Leveraging data-driven innovations, such as data transformation, sharing, and visualization, to enhance understanding and communication of research outcomes.

In conclusion, this project aims to develop a monitoring framework to track HPAIV emergence and spread globally, employing advanced modeling techniques and data-driven approaches. The research outcomes will contribute to our understanding of zoonotic disease dynamics, improve risk prediction frameworks, and guide prevention and control strategies for the benefit of various sectors and public health.

Training and skills

Dr Lu Lu and Dr Samantha Lycett from the Roslin Institute (UoE) will provide supervision on phylodynamic and mathematical modeling, enabling a comprehensive understanding of the evolution and spread of highly pathogenic avian influenza viruses (HPAIVs). Prof. Ian Simpson from the Institute for Adaptive & Neural Computation in the School of Informatics (UoE) will contribute expertise in machine learning modeling, biochemistry, and genetics.

The four-year PhD project offers an array of career development opportunities, skills acquisition, and comprehensive training. The student will receive extensive training in phylodynamic modeling techniques under the guidance of experts, enabling them to analyze the genetic evolution, transmission dynamics, and spread patterns of highly pathogenic avian influenza viruses (HPAIVs). This training will enhance their computational and statistical skills, which are vital in modeling infectious diseases.

The collaboration with experts in data science will provide the student with training in this highly sought-after skill, allowing them to analyze complex datasets and identify influential drivers of HPAIV spread. Additionally, the project will provide opportunities to develop expertise in incorporating environmental factors, land use, biodiversity, human behavior, and vaccination policies into statistical models, enabling a comprehensive understanding of the impact of these factors on HPAIV emergence and spread.

Overall, the project will provide the student with valuable interdisciplinary training, including expertise in modeling, data analysis, and the application of scientific findings to public health and other sectors. The acquired skills and knowledge will position the student for a successful career in academia, research institutions, or public health organizations, where they can contribute to the prevention and control of zoonotic diseases and address the global challenges they present.