AI could help detect and investigate foodborne illness outbreaks

UKHSA is exploring the role that artificial intelligence (AI) could play in helping scientists to detect and investigate foodborne illness outbreaks.

In a new study, UKHSA experts have assessed different types of AI for their ability to detect and classify text in online restaurant reviews, which could one day be used to identify and potentially target investigations into foodborne illness outbreaks.

Foodborne gastrointestinal (GI) illness – which usually presents as vomiting and diarrhoea – is a major burden on society’s health in the UK, causing millions of people to become unwell every year. However, it is estimated that most cases of GI illness are not formally diagnosed.

UKHSA tech experts and scientists looked at a range of large language models and rated their ability to trawl thousands of online reviews for information about symptoms which might relate to GI illness – such as diarrhoea, vomiting and abdominal pain – as well as different food types people report eating.

UKHSA scientists believe that gathering information in this way could one day become routine, providing more information on rates of GI illness which are not captured by current systems as well as vital clues around possible sources and causes in outbreaks.

However, the study has highlighted key challenges around the approach that would need to first be overcome, particularly around access to real-time data.

While it is possible to use the approach to gather general information on the type of food people have eaten and which may be linked to illness, determining which specific ingredients or other factors that may be linked is difficult. Variations in spelling and the use of slang were also identified as potential challenges, as well as people misattributing their illness to a given meal.

Professor Steven Riley, Chief Data Officer at UKHSA said

We are constantly looking for new and effective ways to enhance our disease surveillance.

Using AI in this way could soon help us identify the likely source of more foodborne illness outbreaks, in combination with traditional epidemiological methods, to prevent more people becoming sick.

Further work is needed before we adopt these methods into our routine approach to tackling foodborne illness outbreaks.

While work has previously been carried out to consider how AI could help in reviewing restaurant reviews in this way, UKHSA’s latest study goes a step further by looking at a much more detailed list of terms and language that could potentially help to identify illness outbreaks.

This works forms part of UKHSA’s evaluation of AI to perform different tasks within public health.

Over three thousand reviews were manually annotated by epidemiologists after being collected and filtered.

Reviews were filtered for those containing a comprehensive list of possible GI related keywords, which were then further examined for relevant symptoms.

Symptoms such as headache, fever, and respiratory symptoms were not annotated, because they are not sufficiently specific to GI illness.