Artificial intelligence might be able to spot the next animal-to-human virus before it becomes a pandemic.
More scientists than not believe coronavirus which continues to wreak havoc across the globe came from bats in a process known as zoonosis.
Like Covid-19, most new human infectious diseases come from animals – which with the right technology – scientists say could be detected in time to save lives.
Nardus Mollentze, Simon Babayan, and Daniel Streicker at the University of Glasgow suggest that with the right genetic material, a machine may be able to predict what viruses in animals will infect humans.
Their study published in PLOS Biology on Tuesday could prove to be a major breakthrough as of the estimated 1.67 million animal viruses, only a select few can affect us.
The authors wrote: "These findings add a crucial piece to the already surprising amount of information that we can extract from the genetic sequence of viruses using AI techniques."
According to the report, the AI developed by the researchers could have helped identify Covid-19 before it took a single life in Wuhan, China, towards the end of 2019.
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Participating scientist, Mollentze told The Daily Beast: "The ability to predict whether a virus can infect humans from just a genome sequence, while still working reliably for completely new viruses not seen by the model, sets it apart from other approaches."
He and his team in Glasgow were helped by research earlier this year from scientists at the University of Liverpool who looked at AI's potential in the field of animal-human viruses.
According to SciTechDaily, the new report says: "Our findings show that the zoonotic potential of viruses can be inferred to a surprisingly large extent from their genome sequence.
"By highlighting viruses with the greatest potential to become zoonotic, genome-based ranking allows further ecological and virological characterisation to be targeted more effectively.”
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"A genomic sequence is typically the first, and often only, information we have on newly-discovered viruses, and the more information we can extract from it, the sooner we might identify the virus’ origins and the zoonotic risk it may pose.
"As more viruses are characterised, the more effective our machine learning models will become at identifying the rare viruses that ought to be closely monitored and prioritised for preemptive vaccine development."
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