Artificial Intelligence and covid-19 (Image credit- The BMJ)
Following the COVID-19 pandemic, researchers from all over the world have been working nonstop to create fresh methods and innovations that will aid in averting the subsequent worldwide health emergency.
A group of researchers at the Université de Montréal under the direction of Timothée Poisot, a professor in the Department of Biological Sciences, have made one such advancement.
In order to more accurately forecast which viruses are most likely to spread from animals to people, Poisot and his colleagues have been working on an algorithm for predicting mammal-virus interactions.
Over 60% of all infectious diseases in humans are caused by zoonotic diseases, according to the World Health Organization (WHO).
Predicting which combinations of viruses and mammals are most likely to cause zoonotic epidemics is a difficult endeavor, however, given the large number of mammal species and the little knowledge that is currently known about how they interact.
The Poisot algorithm is used in this situation. The program sorts through existing data on mammal-virus interactions using machine learning to determine which combinations are most likely to happen.
After employing statistical analysis to verify these hypotheses, the researchers concentrate their monitoring efforts on the interactions and areas that pose the greatest risk.
The team’s research has already produced some unexpected results. For instance, they identified the possibility of human infection by mouse ectromelia, a virus similar to smallpox in mice.
This discovery highlights the need of keeping an eye on even obscure viruses and their interactions with mammals.
The Amazon basin in South America, where novel and original interactions between hosts and viruses are more likely to be observed, and Central Africa, where new hosts have been discovered that may potentially carry zoonotic viruses, were chosen as the two geographic areas on which the researchers will concentrate their efforts.
The algorithm developed by Poisot and his coworkers is intended to help stop the next pandemic. They think they can help stop zoonotic infections before they have a chance to spread by identifying and keeping an eye on the relationships and areas that present the most risk.
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According to Poisot, the algorithm marks a significant advancement in our knowledge of zoonotic illnesses and how to stop them. The locations where we must travel and learn are really altering, he remarked.
We are now able to find novel and unexpected interactions that could have significant effects on human health instead of merely looking at the typical suspects.