A One Health Computational Network: Integrating Genomic, Population and Ecological Data for Epidemic Preparedness
Lead Research Organisation:
University of Glasgow
Department Name: College of Medical, Veterinary, Life Sci
Abstract
Emerging viruses pose an ever-present and increasing threat to societies globally. Highly connected international transport networks, population growth and movement, coupled with climate and land use change, mean that viruses can spread more rapidly and widely than ever before, reaching new geographical areas and hosts. This is not only a concern for humans and animal health but also for agriculture, as plants are also critical to ecosystems and food security. New interlinked infrastructures and technologies are needed to address these escalating risks.
We propose to establish an interdisciplinary "One Health Computational Network (OHCN)" to develop new computational and data-driven approaches to better predict, detect, understand and prevent emerging viral diseases of humans, animals and plants. Advances in computational methods, in particular artificial intelligence (AI), are providing significant opportunities to enhance our view of the emerging infectious diseases that pose a significant threat to the health and wellbeing of the UK population.
The aim of the OHCN network will be to capitalise on these opportunities by building linked cross-sectoral datasets and developing and applying new computational approaches to tackle a range of viral threats to animal, human and plant health. We will establish an interdisciplinary research network to:
Identify datasets and opportunities to link these across sectors and identify gaps where such data do not currently exist.
Develop computational approaches that can be used to address questions across three central research themes.
Theme 1: The prediction of risk and spread of (re)emerging viruses within susceptible human, animal and plant populations, incorporating the impact of ecosystem and social change.
Theme 2: The assessment of risk and early detection of emerging viruses at the human-animal interface using enhanced surveillance methods.
Theme 3: The prediction of evolutionary change that would impact the phenotype and virulence of (re)emerging viruses.
Computational and data-driven approaches have the potential to predict and inform interventions to prevent the emergence of viral disease by identification of host, vector and environmental contexts that promote cross-species transmission. Computational tools developed by the applicants and other partners include variant nomenclature, dashboards for the identification of key mutations and variant growth rates, and the CLIMB environment for data sharing and analysis.
One of the most important lessons from the SARS-CoV-2 pandemic was the need for rapid response driven by science-informed decision making. Mathematical models were key to predicting the spread and consequences of the virus, and genome sequencing coupled with bioinformatics to the understanding of new variants. A paradigm shift in AI, specifically, large language models, has the potential to rapidly summarise extensive bodies of existing knowledge and inference of phenotypic properties from sequence data alone, providing new perspectives on viral biology and pandemic risk.
While similar computational approaches are applicable in animal, human and plant viral diseases, these research communities rarely work together, based on funding requirements. Bringing researchers together working on pathogens in different domains and working at different scales (including molecular, population, species, ecological) will create new opportunities for interdisciplinary working.
To catalyze our activities, we will hold two virtual stakeholder workshops and an in-person symposium to identify opportunities to develop innovative approaches for enhanced monitoring of virus threats, to human, animal and plant populations. We will hold monthly meetings, covering each of the key themes and develop an application for phase II of the interdisciplinary epidemic preparedness call.
We propose to establish an interdisciplinary "One Health Computational Network (OHCN)" to develop new computational and data-driven approaches to better predict, detect, understand and prevent emerging viral diseases of humans, animals and plants. Advances in computational methods, in particular artificial intelligence (AI), are providing significant opportunities to enhance our view of the emerging infectious diseases that pose a significant threat to the health and wellbeing of the UK population.
The aim of the OHCN network will be to capitalise on these opportunities by building linked cross-sectoral datasets and developing and applying new computational approaches to tackle a range of viral threats to animal, human and plant health. We will establish an interdisciplinary research network to:
Identify datasets and opportunities to link these across sectors and identify gaps where such data do not currently exist.
Develop computational approaches that can be used to address questions across three central research themes.
Theme 1: The prediction of risk and spread of (re)emerging viruses within susceptible human, animal and plant populations, incorporating the impact of ecosystem and social change.
Theme 2: The assessment of risk and early detection of emerging viruses at the human-animal interface using enhanced surveillance methods.
Theme 3: The prediction of evolutionary change that would impact the phenotype and virulence of (re)emerging viruses.
Computational and data-driven approaches have the potential to predict and inform interventions to prevent the emergence of viral disease by identification of host, vector and environmental contexts that promote cross-species transmission. Computational tools developed by the applicants and other partners include variant nomenclature, dashboards for the identification of key mutations and variant growth rates, and the CLIMB environment for data sharing and analysis.
One of the most important lessons from the SARS-CoV-2 pandemic was the need for rapid response driven by science-informed decision making. Mathematical models were key to predicting the spread and consequences of the virus, and genome sequencing coupled with bioinformatics to the understanding of new variants. A paradigm shift in AI, specifically, large language models, has the potential to rapidly summarise extensive bodies of existing knowledge and inference of phenotypic properties from sequence data alone, providing new perspectives on viral biology and pandemic risk.
While similar computational approaches are applicable in animal, human and plant viral diseases, these research communities rarely work together, based on funding requirements. Bringing researchers together working on pathogens in different domains and working at different scales (including molecular, population, species, ecological) will create new opportunities for interdisciplinary working.
To catalyze our activities, we will hold two virtual stakeholder workshops and an in-person symposium to identify opportunities to develop innovative approaches for enhanced monitoring of virus threats, to human, animal and plant populations. We will hold monthly meetings, covering each of the key themes and develop an application for phase II of the interdisciplinary epidemic preparedness call.
