Machine learning algorithm being used to track Ebola infested bats

A team of scientists from the Cary Institute of Ecosystem Studies is using a machine learning algorithm to predict which bat species are most likely to carry Ebola.

An article from Digital Trends notes that, over the past year or so, there have been a number of tech projects aimed at stopping the spread of Ebola, but this one in particular involves plotting the possible spread of Ebola and other filoviruses of the same family by predicting which species of bat are most likely to carry these diseases.

"This work entailed collecting intrinsic features describing the world’s bat species — 1,116 species altogether — and training a machine learning algorithm on these data to learn which features best predict the bat species that carry filoviruses," lead author of the study Barbara Han, a disease ecologist at the Cary Institute of Ecosystem Studies, told Digital Trends. "The algorithm then identifies other bat species sharing a similar trait profile to the filovirus-positive species."

The machine learning model creates profiles of bat species likely to harbor filoviruses, based on the 21 bat species that have previously exhibited such tendencies in the past. Analyzing 57 different variables, the algorithm was able to predict which bats act as potential carriers with 87% accuracy, and the predicted bat species were then plotted onto a world map (pictured).

Machine learning techniques are being utilized quite a bit lately, and in a wide variety of applications. Check out some of the latest articles we’ve written that pertain to machine learning technologies and products:

• Google acquires image recognition company Moodstocks
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• Twitter invests in machine learning capabilities with acquisition of Magic Pony Technology
• Computer vision software development: Current status and future trends
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View the Digital Trends article.
View more information on the Cary Institute for Ecosystem Studies.

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