Avian influenza (Influenza A virus) is an increasing threat to both livestock and human health
through potential zoonotic transmission. Although avian influenza infections are is ultimately
seeded in from wild birds such as waterfowl and shorebirds, the dynamics of in these wild hosts
is still poorly understood.

The project aims to use ecological and evolutionary models including machine learning to
investigate a) the landscape-level patterns of avian influenza spread among wild bird hosts and
b) genetic variation across avian influenza in wild hosts to identify strains likely to become
zoonotic in future.

Using open-access geospatial records of avian influenzavirus surveillance in wild birds, we will
construct species distribution models to understand which environmental and host traits (e.g.,
host species diversity, physical environment, climate) drive the presence of avian influenza in
Eurasia, before expanding models out to global coverage. Further models may be produced and
validated to map risk of specific strains, e.g., subtypes associated with HPAI.

We will also extract influenza A virus genomes from sequence repositories and train machine
learning models on features of nucleotides and proteins (e.g., genome composition, protein
secondary structure) to predict zoonotic capability. Further models will explore ‘end-to-end’
learning, using techniques that can automatically identify important features from genomes (e.g.,
convolutional neural networks).

Both subprojects will particularly aim to understand and statistically quantify the roles of waterfowl
compared to the much less-studied gulls and shorebirds as wild hosts. More widely, research
outcomes of this project will act as risk assessment systems to understand which avian
influenzavirus will present high risks of human infection, and where such viruses are likely to
circulate.

We are recruiting for a postdoctoral research associate to work with Dr Liam Brierley and
Professor Matthew Baylis on a project funded by CSL’s Seqirus, an industrial partner of The
Pandemic Institute on ‘Modelling the spread of avian influenza and risk of zoonotic spillover via
machine learning’. The post is available for 15 months at UoL salary grade 7, starting 1st Feb
2023. You will be directly line managed by Dr Brierley with additional reporting to Professor
Baylis. You will also present updates to other University of Liverpool colleagues and the funding
partners.

You will be a member of the Department of Livestock and One Health within the Institute of
Infection, Veterinary & Ecological Sciences. The postholder will be responsible for handling a
diverse range of datasets describing avian influenzaviruses and their hosts (for example, host
phylogenetic diversity and community composition, life history and behavioural trait data,
climatic and environmental spatial grids, viral nucleotide and protein sequences). They will be
expected to develop robust and innovative analytical models applying a range of machine
learning techniques to understand the dynamics of avian influenza, though they may opt to take
more of a lead in subproject a) or b), depending on their experience and interest.

You will also be expected to interpret model findings using expert insight in both statistical
validation and the epidemiological context. They will play a role in disseminating the results
through written manuscripts and research presentations.

You will also connect across disciplines to other research teams within the university working
on modelling avian influenza (colleagues in Mathematical Sciences, Computer Science, and
Health Data Science).