The UK Research and Innovation (UKRI) has appointed the Infection Innovation Consortium (iiCON) to deliver a £1.5m initiative aimed at accelerating approaches to infection transmission.
iiCON, which is led by the Liverpool School of Tropical Medicine, will support the formation of cross-disciplinary networks to accelerate the development of solutions that leverage technologies such as artificial intelligence (AI), digital and automation, advanced humanised infection models and novel diagnostics.
Professor Janet Hemingway, founding director of iiCON, said: “Combatting the transmission of infection is one of the key health challenges of our time and one that is growing in urgency.
“As such, it’s critical that we leverage novel, disruptive technology to drive forward our collective efforts to tackle the spread of infection.”
Hemingway added that the consortium was “particularly keen” to engage individuals and companies who have not previously worked in the area.
Companies and groups will have the opportunity to apply to take part in two ‘sandpit’ workshops held in Liverpool on 14 March and London on 9 May, which will help stimulate innovation and create collaborative approaches to tackle the spread of infection.
The events, complementing the work of UKRI’s antimicrobial resistance and epidemic preparedness programmes, will shape the call for funding pots of £50,000 to £150,000 to test approaches to tackling infections, with network support grants of up to £50,000 also available.
“We hope this exciting programme will spark the formation of new cross-disciplinary networks and support participants to shape the future direction of our response to infection transmission – bringing forward novel concepts and approaches that may hold the key to unlocking this critical issue,” Hemingway said.
The announcement comes less than one month after the UKRI’s Engineering and Physical Science Research Council invested £80m into nine new UK research hubs to deliver AI technologies.
Six of the hubs will focus on AI for science, engineering and real-world data and will provide the tools needed to advance future AI innovations and accelerate their application in key areas such as healthcare. The remaining three will address mathematics and computational research to increase understanding of efficient AI systems.