Which emerging methods (for example, diagnostics, genomics, and data analytics), will help the FSA most effectively detect sources of infection or the emergence of new microbiological hazards in the food supply chain?
Background
Foodborne disease (FBD) is a major public health risk with 2.4 million individual illnesses and more than 16,000 hospitalisations per year. It imposes an annual burden on society equivalent to £9.1 billion. The majority of human foodborne disease is caused by a handful of pathogens (including norovirus, campylobacter, salmonella, Shiga toxin-producing Escherichia coli (STEC) and listeria) which, in most cases, enter the food chain from farmed animals or the environment.
In addition to FBD, the agri-food supply chain also poses a risk for the spread of antimicrobial resistance (AMR). Addressing the public health threat posed by AMR is an ongoing strategic priority for the UK and the Government has recently published its new 5-year AMR National Action Plan (NAP) 2024-29 (Opens in a new window), which sets out actions to slow the development and spread of AMR.
The overarching aim of this research priority is to provide evidence to enable the FSA to better control the spread of FBD and AMR within the food supply chain. For both threats, taking a ‘One Health’ approach is important, to understand the sources (e.g. livestock) and routes (e.g. food and environment) of infection and ultimately the impact (e.g. on humans).
As well as characterising new and emerging threats, we need to build our current understanding as to the attribution, prevalence, and nature of existing FBD and AMR risks, filling key evidence gaps which will support improved control measures and enhance food hygiene policy. We are also seeking to build capability in this area through development of new surveillance methods, which in turn can support the FSA’s work on trade and border inspections, as well as supporting broader disease and incident management.
Next steps
Get in touch: andrew.downie@food.gov.uk
Source
This question was published as part of the set of ARIs in this document:
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Related UKRI funded projects
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FightAMR: Novel global One Health surveillance approach to fight AMR using Artificial Intelligence and big data mining
Understanding the risk and direction of antimicrobial resistance (AMR) spread through food-borne routes, and developing of interventions to limit the spread of AMR within and between humans, animals, environment and food...
Funded by: MRC
Why might this be relevant?
This project specifically focuses on detecting AMR spread through food-borne routes using AI and big data mining, aligning with the question's emphasis on emerging methods for detecting sources of infection.
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Development of the PurifAI Data Dashboard: An integrated, AI-based platform for Real-Time Diagnostics and Analytics in Food Safety.
Foodborne illnesses affect 600 million people globally each year, causing 420,000 deaths and significant economic losses across the food supply chain. Contamination incidents cost the global food industry billions annual...
Funded by: Innovate UK
Lead research organisation: AMPED PCR LTD
Why might this be relevant?
Partially relevant as it focuses on diagnostics and analytics in food safety, but not specifically on detecting sources of infection or new microbiological hazards.
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Novel global One Health surveillance approach to fight AMR using Artificial Intelligence and big data mining
Understanding of the risk and direction of antimicrobial resistance (AMR) spread through food-borne routes, and development of interventions to limit the spread of AMR within and between humans, animals, environment and ...
Funded by: Innovate UK
Why might this be relevant?
Fully relevant as it specifically addresses the detection of AMR spread in the interconnected human-animal-environment-food system.