In a new study, researchers used machine learning to make accurate forecasts of whether animals carry dangerous pathogens. The researchers say the predictions could help experts improve how they prevent and respond to disease outbreaks.
The researchers developed software to analyze a large database of mammalian habits and habitats, including the geographic range and reproductive strategies for hundreds of species. The program evaluated 86 variables, including body size, life span, and population density, to identify patterns common among animals known to carry zoonotic diseases.
Team leader Barbara Han at the Cary Institute of Ecosystem Studies and her colleagues restricted their analysis to rodents, which carry a disproportionately high number of zoonotic diseases. Han and her team first used the program to identify lifestyle patterns common to rodents harboring diseases such as black plague, rabies, and hanta virus, and found their model had an accuracy rate of 90 percent. After the machine "learned" the telltale signs, the researchers searched for new rodents that fit the profile but were not previously thought to be carriers.
The researchers say the program already has found more than 150 new animal species that could harbor zoonotic diseases. The program also predicted 58 new infections in rodents that were already known to carry one zoonotic disease.
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