Mon, 26 February 2024

Newsletter sign-up

Subscribe now
The House Live All
Major new report reveals pathways to better neurological care Partner content
By Roche Products Ltd
Culture shift: tackling antimicrobial resistance from agriculture to operating table Partner content
Taking learnings from the pandemic to improve global trade rules for health Partner content
Stronger foundations: transforming neurological care Partner content
By Roche Products Ltd
Press releases

Government Has Scaled Up “Cough In A Box” Plans To Detect Covid-19 With Algorithms

Researchers claim the sound of a forced cough can be analysed to reveal if you have Covid-19 (Alamy)

2 min read

The government has expanded its ongoing trial of so-called “cough in a box” technology, which uses artificial intelligence (AI) to detect Covid-19 by analysing the sound of people coughing.

Researchers at the Massachusetts Institute of Technology (MIT) revealed in October 2020 that they had successfully detected cases of Covid-19 in asymptomatic individuals simply by processing recordings of an individual’s cough.

Their discovery opens up the possibility of identifying potential coronavirus cases through an individual’s mobile phone without the need for immediate testing. 

UK ministers quickly jumped on the technology in late 2020, awarding two contracts totalling £118,000 to Fujitsu in December to explore potential applications. 

The Department of Health and Social Care (DHSC) has now awarded a further contract worth £119,000 to Ipsos Mori for “scaled up data collection”, with the initial trial now being expanded to include those taking part in the REACT1 Covid-19 prevalence survey.

Those asked to take part in the next stage of the trial will be required to submit audio samples “immediately after they have submitted test swabs” under plans detailed in the latest contract.

A government spokesperson confirmed that a trial into the “cough in a box” tech is ongoing.

“The UK is at the forefront of innovative research to expand our collective understanding into COVID-19 and we are hugely grateful to the thousands of volunteers who participate in trials and studies,” they said. 

“The Joint Biosecurity Centre is working with the Alan Turing Institute and the Royal Statistical Society to research the use of algorithms to detect COVID-19 based on vocal biomarkers available in audio recordings provided by volunteers.”

According to the creators of the algorithm, researchers were able to detect cases of the virus by training the AI model on over 70,000 recordings submitted by volunteers, which included around 2,500 from people confirmed to have Covid-19.

The team at MIT found that, when new cough sounds were introduced, the algorithm accurately identified 98.5% of coughs from people who were confirmed to have Covid-19, including 100% of coughs from people with no symptoms.

“We think this shows that the way you produce sound changes when you have Covid, even if you’re asymptomatic,” said the study’s co-author Brian Subirana, a research scientist in MIT’s Auto-ID Laboratory.

He continued: “The effective implementation of this group diagnostic tool could diminish the spread of the pandemic if everyone uses it before going to a classroom, a factory, or a restaurant.”

Subirana’s team is now working on incorporating the model into a mobile app which they claim could become “a free, convenient, noninvasive prescreening tool” if adopted on a large scale. 

PoliticsHome Newsletters

PoliticsHome provides the most comprehensive coverage of UK politics anywhere on the web, offering high quality original reporting and analysis: Subscribe

Read the most recent article written by Eleanor Langford - Who Is Going On Strike And When In February?




Coronavirus Health
Partner content
Connecting Communities

Connecting Communities is an initiative aimed at empowering and strengthening community ties across the UK. Launched in partnership with The National Lottery, it aims to promote dialogue and support Parliamentarians working to nurture a more connected society.

Find out more