Thursday July 18, 2019

Researchers Develop Model Using Machine Learning to Identify Bat Species that can Spread Nipah Virus

Once infected, people can spread the virus directly to other people, sparking an outbreak. There is no vaccine and the virus has a high mortality rate

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For the study, Machine Learning, a form of Artificial Intelligence, was used to flag bat species with the potential to harbour Nipah. Wikimedia Commons

Researchers have developed a model using Machine Learning (ML) to identify bat species with the potential to host the Nipah virus, with a focus on India. Four new bat species were flagged as surveillance priorities.

“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 Barbara Han from Cary Institute of Ecosystem Studies in the US.

India is home to an estimated 113 bat species. Just 31 of these species have been sampled for the Nipah virus, and 11 have been found to have antibodies that signal host potential, according to the study published in the journal PLOS Neglected Tropical Diseases.

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For the study, the research team compiled published data on bat species known to carry Nipah and other henipaviruses globally. Wikimedia Commons

The 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. Domestic pigs are also bridging hosts that can infect people.

Once infected, people can spread the virus directly to other people, sparking an outbreak. There is no vaccine and the virus has a high mortality rate. For the study, Machine Learning, a form of Artificial Intelligence, was used to flag bat species with the potential to harbour Nipah.

“By looking at the traits of bat species known to carry Nipah globally, our model was able to make predictions about additional bat species residing in India with the potential to carry the virus and transmit it to people. These bats are currently not on the public health radar and are worthy of additional study,” Han said.

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Data included 48 traits of 523 bat species, including information on foraging methods, diet, migration behaviours, geographic ranges and reproduction. Pixabay

For the study, the research 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 behaviours, geographic ranges and reproduction.

During the study, their algorithm identified known Nipah-positive bat species with 83 per cent accuracy. It also identified six bat species that occur in Asia, Australia and Oceania that have traits that could make them competent hosts and should be prioritised for surveillance. Four of these species occur in India, two of which are found in Kerala.

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“We set out to make trait-based predictions of likely henipavirus reservoirs near Kerala. Our focus was narrow, but the model was successful in identifying Nipah hosts, demonstrating that this method could serve as a powerful tool in guiding surveillance for Nipah and other disease systems,” said Raina K. Plowright from Montana State University in the US.

“Identifying which species harbour disease is an important first step in surveillance planning. We also need to prioritise research on which virus strains pose the greatest risk to people. Ultimately, the goal is to extinguish risk, not fight fires,” Han concluded. (IANS)

Next Story

Artificial Intelligence, Machine Learning Help Shrimp, Vegetable Farmers Reap Good Harvest

Aibono works with about 500 farmers and has about 200 acres are under active cultivation

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A team from Germany, the United States and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images.
There are about 232,000 new cases of melanoma, and 55,500 deaths, in the world each year, the research added.

Artificial Intelligence (AI) and machine learning have entered aquaculture and agriculture farms in some states benefitting the farmers in cutting down their labour and the uncertainties of trial and error methods.

Thanks to artificial intelligence and machine learning technologies used by companies like the city-based Coastal Aquaculture Research Institute (CARI) and Aibono Smart Farming Pvt Ltd, Bengaluru, shrimp and vegetable farmers are able to increase their yield, cut their costs and have better market access.

V. Geetha, who practices aquaculture in Andhra Pradesh, told IANS: “Before signing up for CARI’s ‘FarmMOJO’ — an AI app-enabled farm advisor tool — we used to jot down the critical data in a notebook and act on it. But we wouldn’t know how much to feed the shrimp. There would either be over or under feeding.”

Since he tied up with CARI six months back, Prakash said, the company takes care of water quality tests in his pond and all the required data is available on his mobile with suggested action to be taken.

“Earlier the quantum of feed used would differ. Now we use the correct feed quantity, which has reduced the feed cost. The cost of medicines has also decreased. Earlier many would suggest several things. Now we go by what CARI says,” Prakash remarked.

“At 600,000 ton a year, India’s shrimp exports stand at Rs 45,000 crore annually. But no major technology was used so far in shrimp farming,” Rajamanohar Somasundaram, Co-Founder and CEO, CARI told IANS.

He said, now that the shrimp farming has been digitised, the data collected has been fed into the FarmMOJO programme.

“From the data, we have built machine learning. The software advises farmers on the use of feed, medicines and other things. The tool also helps in predicting the chances of a disease outbreak in the client farm based on the data available from other ponds,” he said.

The company has two revenue streams viz., subscription fee for FarmMOJO and commission on sales of products of partner companies. “At present, we have about 750 ponds spread over Tamil Nadu, Andhra Pradesh, Gujarat and Odisha. We will soon enter West Bengal.This year we plan to cover 2,500 ponds,” Somasundaram said.

Farmers, India
An Indian woman helps her farmer husband irrigate a paddy field using a traditional system, on the outskirts of Gauhati, India, Feb. 1, 2019. VOA

In agriculture, Bengaluru-based Aibono Smart Farming Pvt Ltd and its AI product are helping the farmers in Nilgiris district of Tamil Nadu to match the supply and demand of hill vegetables.

“In India, the land holdings by farmers are small. So, precision farming could be used only if the supply and demand are matched. The other problem is, good price realisation if the yield is good. Farmers do not have a foresight on what to produce and when,” Vivek Rajkumar, Founder told IANS.

Rajkumar said fruits and vegetables are a $250 billion market in India far bigger than that of fast moving consumer goods. But there are no e-commerce players in this segment.

“Aibono is like a dairy cooperative. It assures farmers of buying every kilogram of their produce at a good price so that they can make money. The average land holding of the farmers in the network ranges between 0.5 to 1.5 acre,” Rajkumar said.

“We collect about 2,000 data points like weather, soil tests, photographs etc. Open farm is like a factory without a roof. It is dynamic. But farmer’s activities are routine and predictable. But agronomy has to be changed to dynamic mode,” Rajkumar said.

With farmers seeing increasing yield but not commensurate increase in realisation, Aibono decided to look at the demand side and started to study the consumption pattern.

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“At the retail end, people buy a fixed quantity of the vegetables. The buying pattern in predictable but it is the supply that varies,” Rajkumar said.

“We signed up with retailers and hotels assuring them of supplies. For the farmers, we started calibrating issue of seeds so that the supplies could be assured at certain quantities at a specified time,” Rajkumar said.

Aibono works with about 500 farmers and has about 200 acres are under active cultivation. Rajkumar said: “we about 300 retailers in its network and next year the number is set to grow several times. We charge Re 1 per kg as fee for service to the farmers.” (IANS)