Artificial intelligence is being employed by mathematicians at Oxford University and Manchester University to uncover new variants of Covid-19.

This AI framework could potentially assist in tracking other infectious viruses in the future.

Mathematicians at The University of Manchester invented an algorithm known as CLASSIX that identifies groups of viral genomes with the help of machine learning.

Genomes suspected to present risks in the future can be spotted within massive data volumes.

The researchers' findings suggest this method could be a valuable addition to conventional techniques in tracking viral evolution.

Dr Roberto Cahuantzi, a researcher at the University of Manchester, said: "Since the emergence of Covid-19, we have seen multiple waves of new variants, heightened transmissibility, evasion of immune responses, and increased severity of illness.

"Scientists are now intensifying efforts to pinpoint these worrying new variants, such as alpha, delta and omicron, at the earliest stages of their emergence.

"If we can find a way to do this quickly and efficiently, it will enable us to be more proactive in our response, such as tailored vaccine development and may even enable us to eliminate the variants before they become established."

RNA viruses like Covid-19 evolve extremely rapidly due to their high mutation rate and the short period between generations.

Currently, the Global Initiative on Sharing All Influenza Data database contains nearly 16 million sequences.

These sequences are processed to chart the evolution and history of all Covid-19 genomes, a process requiring time and computing power.

The new method simplifies this process, allowing automation and reducing the resources required.

Professor Thomas House, of The University of Manchester, said: "The unprecedented amount of genetic data generated during the pandemic demands improvements to our methods to analyse it thoroughly.

"The data is continuing to grow rapidly, but without showing a benefit to curating this data, there is a risk that it will be removed or deleted.

"We know that human expert time is limited, so our approach should not replace the work of humans all together but work alongside them to enable the job to be done much quicker and free our experts for other vital developments."

Machine learning techniques are used to group similar sequences based on word patterns, with the proposed method breaking down genetic sequences into smaller 'words'.

Professor Stefan Güttel, of the University of Manchester, said: "The clustering algorithm CLASSIX we developed is much less computationally demanding than traditional methods and is fully explainable, meaning that it provides textual and visual explanations of the computed clusters."