ML is a type of Artificial Intelligence and can be described as a mathematical model where computers are trained to learn to see connections and solve problems using different data sets. 

The team from the University of Gothenburg in Sweden developed a method to improve testing strategies during epidemic outbreaks and to predict which individuals require the need for testing, even with relatively limited information. In the study, data about the infected individual's network of contacts and other information were used: Who they have been in close contact with, where and for how long. 

The researchers found that the outbreak can quickly be brought under control when the method is used, while random testing leads to uncontrolled spread of the outbreak with many more infected individuals. 

Under real world conditions, the information can be added, such as demographic data and age and health-related conditions, which can improve the method's effectiveness even more. 

The same method can also be used to prevent reinfections in the population if immunity after the disease is only temporary, the researchers said. 

"This can be a first step towards the society gaining better control of future major outbreaks and reducing the need to shut down society," said the study's lead author Laura Natali, a doctoral student of physics at the varsity. 

The method also has the potential to easily predict if a specific age group should be tested or if a limited geographical area is a risk zone, such as a school or a specific neighbourhood. 

"When a large outbreak begins, it is important to quickly and effectively identify the infectious individuals. In random testing, there is a significant risk of failing to achieve this, but with a more goal-oriented testing strategy, we can find more infected individuals and thereby also gain the necessary information to decrease the spread of the infection," Natali said. 

"Machine Learning can be used to develop this type of testing strategy," she added.