Machine Learning Spots Bat Species Likely To Spread Nipah

Scientists have developed a machine learning model that predicts which bat species have the potential to spread the deadly Nipah virus in India.

AsianScientist (Jul. 15, 2019) – Machine learning has helped scientists identify bat species which could host Nipah virus, the cause of lethal outbreaks afflicting people in South and Southeast Asia. These results, published in PLoS Neglected Tropical Diseases, also flagged four new bat species as surveillance priorities.

Nipah virus is a highly lethal, emerging henipavirus that can be transmitted to people from the body fluids of infected bats. Eating fruit or drinking date palm sap that has been contaminated by bats has been flagged as a transmission pathway. Once infected, people can spread the virus directly to other people, sparking an outbreak. Domestic pigs are also bridging hosts that can infect people. There is no vaccine and the virus has a high mortality rate.

“While there is a growing understanding that bats play a role in the transmission of Nipah virus in Southeast Asia, less is known about which species pose the most risk. Our goal was to help pinpoint additional species with a high likelihood of carrying Nipah, to target surveillance and protect public health,” said Cary Institute of Ecosystem Studies’ Dr. Barbara Han, co-lead author of the paper.

India is home to an estimated 113 bat species. Just 31 of these species have been sampled for Nipah virus, with 11 found to have antibodies that signal host potential.

“Given the role bats play in transmitting viruses infectious to people, investment in understanding these animals has been low. The last comprehensive and systematic taxonomic study on the bats in India was conducted more than a century ago,” said study co-lead Assistant Raina K. Plowright of Montana State University.

First, the team compiled published data on bat species known to carry Nipah and other henipaviruses globally. Data included 48 traits of 523 bat species, including information on foraging methods, diet, migration behaviors, geographic ranges and reproduction. They also looked at the environmental conditions in which reported spillovers occurred.

Then they applied a trait-based machine learning approach to a subset of species that occur in Asia, Australia, and Oceana. Their algorithm identified known Nipah-positive bat species with 83 percent accuracy. It also identified six bat species that occur in Asia, Australia and Oceana that have traits that could make them competent hosts and should be prioritized for surveillance. Four of these species occur in India, two of which are found in Kerala.

“As this paper was going to press, another case of Nipah virus was confirmed in Kerala. The public health community has again been forced into reactive mode. Our study is a starting point for the research needed to contain Nipah at its source, so we are managing spillover risk, instead of human suffering,” Plowright said.

The article can be found at: Plowright et al. (2019) Prioritizing Surveillance of Nipah Virus in India.


Source: Cary Institute of Ecosystem Studies; Photo: Rajib Islam.
Disclaimer: This article does not necessarily reflect the views of AsianScientist or its staff.

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