Research

Emerging infectious diseases still pose serious threats to global health, exemplified by the COVID-19 pandemic (SARS-CoV-2) as well as Zaire ebolavirus, Zika virus, and numerous others over the last twenty years. Our research mission is to use new data, methods, and computational technologies to better understand how RNA viruses ‘host shift’ (i.e., infect and cause disease within a new host species) with a view to anticipating future pandemic risks. We use a broad range of methodologies (e.g., biostatistics, artificial intelligence, natural language processing) and model a broad diversity of animal RNA viruses, though our research is driven by consistent key questions around dynamics of viruses in their…

…ecological contexts

If we are to anticipate future human disease outbreaks, we need to better understand dynamics in wild hosts and the environments in which we interact with them. We are interested in modelling diversity of animal hosts and changes to landscapes influence cross-species transmission and emergence in humans, including global distributions of bat-borne viruses and the ecological shifts associated with the 2021 outbreak of highly pathogenic avian influenza.

Collaborators: Dr Sarah Hayes, Prof. Christl Donnelly (University of Oxford); Prof. Matthew Baylis (University of Liverpool)

Ecology and environment predict spatially stratified risk of highly pathogenic avian influenza in wild birds across Europe

Sarah Hayes, Joe Hilton, Joaquin Mould-Quevedo, Christl Donnelly, Matthew Baylis, Liam Brierley

Global discovery of human-infective RNA viruses: A modelling analysis

Feifei Zhang, Margo Chase-Topping, Chuan-Guo Guo, Bram Van Bunnik, Liam Brierley, Mark Woolhouse

Quantifying global drivers of zoonotic bat viruses: a process-based perspective

Liam Brierley, Maarten J Vonhof, Kevin J Olival, Peter Daszak, Kate E Jones

…evolutionary dynamics

Evolutionary adaptation is the driving force underlying host range of a virus. A core theme of our research is understanding how ability to infect specific hosts and tissues can be characterised directly from genome sequences by training artificial intelligence approaches on genomic and proteomic traits (‘‘genotype-to-phenotype’’) - and how these traits evolve over time. We are also interested in whether evolutionary models can highlight viruses likely to be transmissible or virulent in human populations. Being at the intersection of evolution and computer science, our work often necessitates development of new computational methods for viral sequence data.

Collaborators: Dr Jan Gogarten, Dr Sebastien Calvignac-Spencer (Helmholtz Institute for One Health); Dr Lu Lu (Roslin Institute, University of Edinburgh)

Temporal dynamics, discovery, and emergence of human-transmissible RNA viruses

Lu Lu, Feifei Zhang, Liam Brierley, Gail Robertson, Margo Chase-Topping, Samantha Lycett, Mark Woolhouse

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning

Liam Brierley, Anna Fowler

Tissue tropism and transmission ecology predict virulence of human RNA viruses

Liam Brierley, Amy B Pedersen, Mark Woolhouse

…public health impact

Although human populations are continuously exposed to novel viruses, few emerge successfully to cause significant outbreaks. Through collaborations, we are exploring how genomic and machine learning methodologies can improve predicted impact of emerging viruses on public health. This includes modelling chance of discovering new viruses, dynamics of transmissibility within populations, and outbreak characteristics.

Collaborators: Dr Dan Hungerford, Prof. Iain Buchan (University of Liverpool); Dr Jasmina Panovska-Griffiths (University of Oxford)

A systematic review of the prevalence of persistent gastrointestinal symptoms and incidence of new gastrointestinal illness after acute SARS-CoV-2 infection

Michael Hawkings, Natasha Vaselli, Dimitrios Charalampopoulos, Liam Brierley, Alex Elliot, Iain Buchan, Daniel Hungerford

Impact of climatic, demographic and disease control factors on the transmission dynamics of COVID-19 in large cities worldwide

Soeren Metelmann, Karan Pattni, Liam Brierley, Lisa Cavalerie, Cyril Caminade, Marcus SC Blagrove, Joanne Turner, Kieran J Sharkey, Matthew Baylis

…systematic data gaps

and modern solutions

Our collective scientific knowledge of how viruses interact with their hosts is vast, although systematic data gaps exist. Our group is dedicated to understanding these gaps through statistical analysis and resolving them with modern computational tools. A primary focus of our current research is text mining to unlock the huge volume of information on host-parasite relationships in scientific literature. We are exploring natural language processing and large language model methods to generate usable data resources describing viral interactions with hosts, tissues, proteins, and more.

Collaborators: Dr Maxwell Farrell (University of Glasgow); Dr Rory Gibb (UCL)

The changing landscape of text mining: a review of approaches for ecology and evolution

Maxwell Farrell, Nicolas Le Guillarme, Liam Brierley, Bronwen Hunter, Daan Scheepens, Anna Willoughby, Andrew Yates, Nicole Mideo

Past and future uses of text mining in ecology & evolution

Maxwell Farrell, Liam Brierley, Anna Willoughby, Andrew Yates, Nicole Mideo

The Global Virome in One Network (VIRION): an atlas of vertebrate-virus associations

Colin Carlson, Rory Gibb, Gregory Albery, Liam Brierley, Ryan Connor, Tad Dallas, Evan Eskew, Anna Fagre, Maxwell Farrell, Hannah Frank, Renata Muylaert, Timothée Poisot, Angela Rasmussen, Sadie Ryan, Stephanie Seifert

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Some of our active research partnerships: