Researchers used machine learning and artificial intelligence technology to predict over 20,000 unknown links between zoonotic diseases and humans. Researchers believe that the recent study could help scientists create future mitigation plans for the spread of zoonotic diseases and livestock diseases caused by currently known viruses.
What are Zoonotic Diseases?
According to the Centers for Disease Control and Prevention, zoonotic diseases are caused by bacteria and germs spread between humans and animals.
Animals provide numerous benefits to humans, and many of us interact with animals on a daily basis. These creatures provide food, travel, livelihood, education, and companionship to many. Millions of American households have one or more pets. On the other hand, there are instances when animals carry harmful germs that spread to humans, which causes illnesses of varying degrees.
Zoonotic diseases are common not just in the United States but across the globe. Scientists estimate that roughly 6 out of every 10 infections in humans can be spread from animals, while 3 out of 4 new infectious diseases in humans come from animals.
Germs that cause zoonotic diseases can be spread via direct contact with an animal's saliva, urine, blood, feces, or other bodily fluids. Indirect contacts, vector-borne, foodborne, or waterborne germs.
20,000 Unknown Links Between Susceptible Mammalian Species and Viruses
Researchers from the University of Liverpool published a study powered by AI predicting over 20,000 unknown links between viruses and susceptible mammalian species, published in the journal Nature Communications, entitled "Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations."
Maya Wardeh, a Ph.D. lead author from the Institute of Infection, Veterinary, and Ecological Sciences, University of Liverpool, explains that viruses move across the globe with no hesitation. The recent model provides an efficient and powerful way to assess potential hosts of zoonotic diseases that have yet to be discovered.
She adds that the foresight provided by the model could assist researchers in identifying and mitigating the spread of zoonotic and animal disease risks.
There are thousands of viruses known to infect mammals; recent studies estimate that less than 1% of mammalian viral diversity has yet to be discovered today. Some of the viruses, like the human and feline immunodeficiency viruses, have a significantly narrow host range, while others like rabies and West Nile virus have a wider host range, reports Gen Magazine.
The team's machine learning approach predicted more than 20,000 unknown links between known viruses and mammalian hosts. This suggests that current knowledge on viruses underestimates the number of links between wild and semi-domesticated mammals.
Data derived from the process indicates that there could be more than 5 times more associations between zoonotic viruses and mammals than what experts previously thought. The model was also able to predict a five-fold increase in links between wild and domesticated mammals and viruses of those economically significant domestic species like livestock and pets.
The team is currently expanding research to predict the ability of insects and ticks to transmit viruses to mammals and birds.
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