A study published yesterday (16) in the scientific journal Nature raised an important question for the understanding and prophylaxis of new forms of coronavirus arising from homologous recombination among infectious agents: which host animals could harbor sources of new forms of the virus?
To answer this question, scientists at the University of Liverpool used machine learning technology to identify, through three complementary perspectives (viral, mammal and network), which allowed distinguishing several high-risk species for coronavirus surveillance.
How was the research done?
Taking into account that coronaviruses constitute an extensive family of viruses, capable of infecting birds and mammals, the researchers used machine learning to predict the relationships between 411 strains of coronavirus from GenBank, the National Institutes of Health database, and 876 host species of mammals.
The calculations allowed scientists to identify animals most likely to be co-infected with different strains of the virus, which could result in the emergence of a new type. The prediction was that there are 11.5 times more associations between species of mammals and strains of coronavirus than previously known.
The researchers also estimated that there are more than 30 times more potential SARS-CoV-2 recombination hosts, and more than 40 times more species that can be infected with four or more coronaviruses than seen so far.
Of the 126 species identified as hosts of the virus, some animals stood out as the Asian civet, predicted for 32 coronaviruses; the horseshoe bat, 67; the intermediate horseshoe bat, 44; and the pangolin, 14. But the absolute champion of SARS-CoV-2 recombination hosting was the domestic pig, capable of harboring 121 new types of coronavirus.
In addition to these highly suspicious hosts, the algorithm revealed other animals that had not been previously associated with the recombination of the virus: Asian yellow bat, chimpanzee, African green monkey, common hedgehog, European rabbit and domestic cat.