Researchers from Georgetown University led an international team of scientists to study the human-to-wildlife disease transmission cases. It is the opposite of the wildlife-to-human disease transmission observed at the start of the COVID-19 pandemic. Some scientists claimed that SARS-CoV-2 originated from bats and transmitted to humans, MailOnline reported.
The team learned that nearly 100 different cases where diseases have undergone spillback from humans to wild animals, similar to how SARS-CoV-2 spread to mink farms, lions, and tigers in zoos, and the wild white-tailed deer. That means humans might have given viruses to animals more often than previously thought.
Human-to-Wildlife Disease Transmission
In their study, titled "Assessing the Risk of Human-To-Wildlife Pathogen Transmission for Conservation and Public Health," published in Ecology Letters, the team described the almost 100 cases of human-to-wildlife disease transmission.
Postdoctoral fellow Dr. Gregory Albery from Georgetown University said that there had been a growing interest in human-to-wildlife pathogen transmission because of the COVID-19 pandemic.
EurekAlert! reported that the team went on to study different literature to see how the process has manifested in the past. He and colleagues found that almost 50% of the incidents identified occurred in captivity, such as zoos, where there is increased interaction between humans and wild animals. In zoos, veterinarians would always keep a close eye on the health of the animals, and that is when the virus most likely makes the jump.
Furthermore, over 50% of the cases were human-to-primate disease transmission. It is unsurprising because humans are closely related to primates. That is why wild populations of endangered great apes are carefully monitored.
Virologist and wildlife veterinarian Dr. Anna Fagre added that the findings support the idea that pathogens are more likely to be detected in places where humans spend a lot of time. On the other hand, it brings into question - which cross-species transmission events could scientists have missed and what could it mean for public health, and the health and conservation of animals.
ALSO READ : Experts Warn Next Pandemic Could Come Any Moment and Call for Immediate Prevention Efforts From Governments
Using AI to Predict Which Species Are At High Risk of Contracting the Virus
In a similar report from Science Daily, Albery and colleagues thought of using artificial intelligence (AI) to predict which species are most likely to get infected with the virus. Since the human-to-wildlife disease transmission gained much interest during the pandemic, researchers looked at the predictions made by other researchers during the early months of the pandemic.
They found that scientists were able to correctly guess more often than not which species would likely get infected with SARS-CoV-2. Dr. Colin Carlson from the Center for Global Health Science and Security at Georgetown University said that the pandemic gave scientists a chance to test some predictive tools that were proven to be effective.
The team used machine learning and data science that studies the science of the host-virus network, which is a new field of science that predicts which viruses can infect humans, which animals host these viruses and when, where, and why these viruses may emerge. The team believes that those insights are critical in understanding why humans tend to share their diseases with animals.
The team concluded that spillover events might be predictable, but there is little knowledge about wildlife diseases for now. Scientists are monitoring SARS-CoV-2 closely, so they can easily detect it when spillback happens. It will be long-term monitoring, but scientists are positive that it will help establish baselines for both human and wildlife health to lay important groundwork for future studies.
RELATED ARTICLE: White-Tailed Deer in State Island Recored As First Wild Animal That Tested Positive With COVID-19 Omicron Variant
Check out more news and information on the Medicine & Health in Science Times.