The rate at which zoonotic diseases infect humans has steadily risen over the last three decades. Emerging diseases, such as Covid-19 and monkeypox, have heightened the need to develop disease ecology tools to forecast when and where outbreaks are most likely to occur. Now, a team of researchers led by the University of South Florida (USF) has developed a new methodology that could predict disease transmission from wildlife to humans, as well as from one wildlife species to another, and determine who is at risk of infection.
This innovative methodology is based on a machine-learning algorithm which identifies the influence of several variables, such as geographical location or climate, on known pathogens. By using only small amounts of information, this system is able to identify community hotspots at risk of infection on both local and global scales.
“Our main goal is to develop this tool for preventive measures,” said co-principal investigator Diego Santiago-Alarcon, an assistant professor of Integrative Biology at USF. “It’s difficult to have an all-purpose methodology that can be used to predict infections across all the diverse parasite systems, but with this research, we contribute to achieving that goal.”
In order to test the reliability and accuracy of the models generated by this technique, the scientists examined three host-pathogen systems – avian malaria, birds with West Nile virus, and bats with coronavirus. Surprisingly, in all three cases, the species most frequently infected were not necessarily the ones most susceptible to the disease. Identifying relevant factors such as climate and evolutionary relationships appeared crucial to pinpoint hosts with higher risks of infection.
By integrating evolutionary, geographic, and environmental factors, the experts identified host species which have previously not been known to be infected by the pathogen under study, thus providing a reliable method to spot susceptible species, and ultimately mitigate infection risks by directing infectious diseases surveillance and field efforts. This cost-effective strategy would likely help medical authorities and governmental agencies to decide where to invest limited disease resources.
“We feel confident that the methodology is successful, and it can be applied widely to many host-pathogen systems,” Professor Santiago-Alarcon said. “We now enter into a phase of improvement and refinement.”
“Humanity, and indeed biodiversity in general, are experiencing more and more infectious disease challenges as a result of our incursion and destruction of the natural order worldwide through things like deforestation, global trade, and climate change,” added study co-author Andrés Lira-Noriega, a research fellow at the Institute of Ecology in Mexico. “This imposes the need of having tools like the one we are publishing to help us predict where new threats in terms of new pathogens and their reservoirs may occur or arise.”
In future research, the team will test the methodology on additional host-pathogen systems and extend the study of diseases transmission to predict future outbreaks. By the end of 2022, the scientists aim to make their tool easily accessible to the scientific community through a user-friendly app.
The study is published in the journal Proceedings of the National Academy of Sciences.