In light of the rising death toll from the coronavirus, which this week spread to the U.S. and was declared a health emergency by the World Health Organization (WHO), it's worth looking at AI's role in curbing the spread of other diseases. Algorithms have not only informed superior intervention and prevention strategies, they've helped optimize the allocation of resources to fight the spread of infection. Algorithms have even detected preliminary signs of an outbreak well before it came to human pathologists' attention. In a study back in 2014, investigators used statistical modeling to evaluate the testing and treatment of HIV in the U.K. and locate people living with the virus who weren't aware of their disease status. The team found that -- even without behavioral changes on the part of people living with HIV -- their approach could reduce new infections by 5%.

IMAGE: This is a map of known and predicted bat hosts of filoviruses, showing hotspots in Southeast Asia. Findings highlight new potential hosts and geographic hotspots worthy of surveillance. So reports a new paper in the journal PLoS Neglected Tropical Diseases. Filoviruses have devastating effects on people and primates, as evidenced by the 2014 Ebola outbreak in West Africa. For nearly 40 years, preventing spillover events has been hampered by an inability to pinpoint which wildlife species harbor and spread the viruses.

Over the course of the past year or so, there have been a number of incredible tech projects aimed at stopping the spread of Ebola. One approach that we've never come across before, however, involves plotting the possible spread of Ebola and other "filoviruses" of the same family by predicting which bat species they're most likely to be carried by. That's exactly the goal of a team of scientists, who recently used machine learning techniques to build just such a model. Their work may help prevent future spillover events in which it is important to predict which species of wildlife help spread contagion. "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, tells Digital Trends.

A team of scientists have developed a model that can predict the likelihood of bat species carrying Ebola and other filoviruses using a machine learning algorithm. Filoviruses are a group of long filament shaped viruses that encode their genome on a single-stranded RNA. Ebola is the most well-known example; other filoviruses include Marburg disease. Both are lethal viruses that are spread by coming into contact with bodily fluids from an infected person. The last Ebola outbreak happened in 2014 and resulted in 11,310 deaths, according to the World Health Organisation.

Scientists are hoping to use Big Data and machine learning to prevent further outbreaks of Ebola, by identifying the likelihood of various bat species carrying the virus. Ebola is what's known as a filovirus, which are long filament-shaped viruses whose genome is encoded on a single strand of RNA. Ebola is the most famous example, but there are others which are just as deadly, such as the Marburg virus that takes its name from an outbreak in the city of Marburg, Germany, in 1967. Ebola, like Marburg, is spread when people come into direct contact with the bodily fluids of infected persons. The most infamous outbreak of Ebola occurred just two years ago, in West Africa in 2014, where 11,310 people died from the disease, the World Health Organization says.

A team of scientists has developed a model that can predict bat species most likely to transmit Ebola and other filoviruses. Findings highlight new potential hosts and geographic hotspots worthy of surveillance. So reports a new paper in the journal PLoS Neglected Tropical Diseases. Filoviruses have devastating effects on people and primates, as evidenced by the 2014 Ebola outbreak in West Africa. For nearly 40 years, preventing spillover events has been hampered by an inability to pinpoint which wildlife species harbor and spread the viruses.