Machine Learning Helps Identify Primate Species Likely to Spread Zika

From Contagion Live
April 8, 2019

Machine learning may be an important tool in controlling and eradicating the Zika virus, according to a recent study that used machine learning to predict the virus among primates in Central and South America.

The study by investigators at the Cary Institute of Ecosystem Studies and IBM was published in the journal Epidemics. The machine learning model identified known flavivirus carriers with 82% accuracy and predicted the risk of Zika among primate species.

“We were surprised to find that very common primate species were predicted to have high risk of carrying mosquito-borne flaviviruses, including Zika virus,” lead author Barbara A. Han, PhD, disease ecologist at the Cary Institute of Ecosystem Studies, told Contagion®. “In Central and South America, the possibility of spill-back infection (from humans to wild primates) is alarming. If Zika virus establishes a sylvatic cycle it could be exceedingly difficult to control.”

Those species with more than 90% risk scores for the virus included species common in developed areas: tufted capuchin, the Venezuelan red howler, and the white-faced capuchin.

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