AI in epidemiology could lead to more efficient identification of the source of foodborne illness, says GlobalData

Following the news that researchers at the University of Georgia developed an Artificial Intelligence (AI) process that can help identify the source of a Salmonella outbreak,

Ana Fernandez Menjivar, Senior Epidemiologist at GlobalData, a leading data and analytics company, offers her view on the impact of this innovation:

“As more machines are able to comb through large amounts of data and highlight the important findings in a short time, we can clearly see how AI is revolutionizing the healthcare sector.

“Innovations like Random Forest introduce a new way of approaching food safety and epidemiology surveillance. By using AI in the process of detecting, characterizing, tracking, and responding to disease outbreaks, disease surveillance becomes automated so decision making is expedited and public health officials can implement real-time interventions to prevent illness.

“The major impact of machine learning for this particular case is that the source of the outbreak can be traced much faster as Random Forest can go through large amounts of Salmonella genome code, and recognize if the source is from food or a food processing settings. For the general population it means that this approach could lead to early identification of the source of an outbreak, and in turn, public health officials can curb the spread of foodborne illness.

“Random Forest is most accurate at predicting the source of Salmonella Typhimurium, which is often associated with animals and animal products that are consumed. In over 80% of the cases it was able to recognize the animal sources of the bacteria.

“As more information is input into the system, the better it becomes at recognizing the source of the infection, which means that investigators will be more confident in linking the outbreak to a specific source.”

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