International research into potential hosts of new viruses that could cause future pandemics could, in the long run, save a lot of money that could be invested in saving lives.
An international study led by scientists at Georgetown University has shown the power of artificial intelligence in predicting which viruses could infect humans, such as the SARS-CoV-2 virus responsible for the Covid-19 pandemic, in which animals could also be found. where could those viruses appear.
The study, published in the scientific journal The Lancet Microbe, is an ensemble of predictable models of potential carriers. The models were confirmed in an 18-month project to identify specific species of bats that are likely to transmit beta-coronavirus, a group of viruses that includes viruses such as SARS.
“If you want to find these viruses, you have to start by profiling their hosts, their habitat, their evolution, and even the shape of their wings,” said Colin Carlson, one of the study’s authors at Georgetown University.
“Artificial intelligence allows us to take data on bats and turn them into concrete predictions: where to look for a new SARS,” Carlson added.
Despite global investments in disease control, it remains difficult to identify and track wildlife reserves and viruses that could one day be transmitted to humans. A new study suggests that the search for closely related viruses could be non-trivial, as it is estimated that about 400 species of bats worldwide have beta-coronavirus. This is a large group of viruses that includes viruses responsible for SARS-KOV (2002-2004) and SARS-KOV-2, due to which we are currently in a pandemic of Covid-19 disease.
Kovis-19 gave impetus to accelerate this research, claims Greg Alberi from Georgetown University. This is a really rare opportunity. Outside of a pandemic, we have never learned so much about viruses in such a short time. A decade of research has been summed up in about a year of study, which means we can actually show that these tools work, he says.
In the first quarter of 2020, a team of scientists practiced eight different statistical models that predicted which animal species could host beta-coronaviruses. For more than a year, the team has been tracking the discovery of 40 new beta-coronavirus host bats to confirm initial predictions and dynamically upgrade their models.
They found that models that use bat data and evolution data work extremely well in predicting new hosts, as opposed to state-of-the-art models that use high-level network science and math.
“Once the potential hosts have been identified, the next step is to invest in monitoring and understanding where and when beta-coronaviruses could be transmitted to humans,” said Daniel Becker of the University of Oklahoma.
Carlson adds that his team is now working with other scientists around the world to test bat specimens of the coronavirus based on the predictions of their models.
If we spend a little money and resources looking for these viruses, we can direct all those resources to things that would actually save lives in the future. We can invest in the development of universal vaccines that target these viruses or in surveillance of areas where humans live near bats. “It’s a win-win for science and public health,” Carlson said.