Machine learning can help stop food poisoning by predicting bacteria strain.

A study carried out by researchers at the University of Edinburgh's Roslin Institute and published in the Proceedings of the National Academy of Sciences which focused on harmful strains of E. coli bacteria could help public health officials to target interventions and reduce risk to human health. The study showed that machine learning can predict strains of bacteria likely to cause food poisoning outbreaks, the researchers used software that compares genetic information from bacterial samples isolated from both animals and people. The software learns the DNA signatures that are associated with E. coli samples that have caused outbreaks of infection in people. It can then pick out the animal strains that have these signatures, which are therefore likely to be a threat to human health. The E. coli strains live in the guts of people and animals without causing illness but E. coli O157 causes more serious human infections. Cows carry E. coli O157 and serve as the main reservoir for this toxic bacteria. The animals excrete the bacteria in their faeces but do not become ill. This makes it difficult to spot which herds and animals are carrying strains that are likely to cause disease in people. The team trained the software on DNA sequences from strains isolated from cattle herds and human infections in the UK and the US. Once trained, the computer is able to predict whether an E. coli strain is likely to have come from a cow or a person. The team used the approach to predict that less than ten percent of the E. coli O157 cattle strains are likely to have the potential to cause human disease. Interventions to stop the spread of the disease by use of vaccines could be targeted at herds with these strains to minimize the risk of outbreaks in people. This study shows the potential of machine learning approaches for identifying these strains early and prevent outbreaks of this infectious disease.