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[YY-EEID US-UK XXXX] Predictive phylogenetics for evolutionary and transmission dynamics of newly emerging avian influenza viruses

Reference	BB/V011286/1
Principal Investigator / Supervisor	Professor Paul Digard
Co-Investigators / Co-Supervisors	Professor Lisa Anne Boden, Dr Samantha Lycett, Professor Lonneke Vervelde
Institution	University of Edinburgh
Department	The Roslin Institute
Funding type	Research
Value (£)	1,091,367
Status	Current
Type	Research Grant
Start date	01/04/2021
End date	31/03/2024
Duration	36 months

Abstract

Influenza A virus poses one of the greatest infectious disease challenges of the 21st Century. It is a ubiquitous avian pathogen with vast antigenic diversity that hinders conventional vaccine approaches, especially in low-value livestock species like poultry. It causes huge economic losses and drains public health budgets. Surveillance programmes generate huge amounts of viral sequence data; surpassing 1 million entries on Genbank. Some aspects of virus behaviour can be predicted from these sequences, but many important facets cannot; this wealth of data therefore represents an underutilised resource. We think that advances in computational approaches mean that the construction of modelling tools with genuine predictive power for the future evolution and spread of avian influenza is possible. To achieve this, we have assembled an international team of experts with interdisciplinary expertise in mathematical modelling, influenza, and the infectious disease-public and animal health interface. Importantly this includes Chinese colleagues who run a surveillance programme in the epicentre of viral diversity. The prediction tool will be the sum of three separate models: one which identifies key viral sequence polymorphisms; one which simulates virus evolution within host under selection pressure; and one that integrate outputs from the first two along with additional inputs from surveillance programs. The primary data inputs are virus sequence information, both at quasi-species and consensus level. We will parameterise the models from existing data (public and unpublished data held by the team) and a series of planned "wet lab" experiments that measure virus fitness. We wish the tool to be of use to stakeholders such as the OIE and WHO as well as small and large poultry holders; development of it will therefore be informed by a series of data collection exercises to get input from these groups of people as to what they require from the scientists.

Summary

Influenza virus is a global problem, causing widespread harm to human health and the food production system because it also infects chickens and pigs. Vaccination is difficult because of the variety and changeability of flu strains found in nature - primarily in wild birds, where often they cause little harm. However, when these strains of virus spill over into domestic poultry or humans, they can cause massive economic losses and fatal disease respectively. In the last twenty years, this has been graphically illustrated by the H5N1 and H7N9 outbreaks. Global surveillance programmes track the virus' movement and as part of this, characterise the sequence of the viral genome. Some aspects of virus behaviour can be accurately predicted from these sequences. However, many other important aspects of virus biology, such as whether it will travel across continents, which species it will infect and whether it will cause serious harm, are much harder to forecast. Our premise is that the volume of sequencing data now available, along with recent advances in computational methods of using such data, will make it possible for the first time to generate virtual models of how the virus will evolve under specific circumstances and how these viral variants will behave. Such models have the potential to produce risk estimates of new strains as they arise that can be used to inform policy and direct strategies to head off impending threats. To achieve this goal, we have brought together a team of international experts with interdisciplinary expertise in mathematical modelling, influenza surveillance and biology, and the infectious disease-public and animal health interface. Importantly, this includes colleagues from China, the likely epicentre of the virus. Together, we will create the computer models that can understand and forecast virus evolution; models that will be made accurate and then tested through a series of focussed laboratory experiments designed to produce the neededdata, and whose types of output will be tailored to the needs of end users through a series of workshops that include the primary stake holders so they can inform the scientists on what information they need.

Committee	Not funded via Committee
Research Topics	Animal Health, Immunology, Microbiology, Systems Biology
Research Priority	X – Research Priority information not available
Research Initiative	Ecology and Evolution of Infectious Diseases - Travel Grants (EEID-TG) [2019]
Funding Scheme	X – not Funded via a specific Funding Scheme

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