Applications of machine learning in animal and veterinary public health surveillance


J. Guitian, M. Arnold, Y. Chang & E.L. Snary

Machine learning (ML) is an approach to artificial intelligence characterised by the use of algorithms that improve their performance at a certain task (e.g. classification or prediction) from data itself and without being explicitly and fully instructed on how to achieve it. Surveillance systems for animal and zoonotic diseases depend upon effective completion of a broad range of tasks, some of them amenable to ML algorithms. As in other fields, the use of ML in animal and veterinary public health surveillance has greatly expanded in recent years. ML algorithms are being used to accomplish tasks that became attainable only with the advent of large datasets, new methods for their analysis and increased computing capacity. Examples include the identification of an underlying structure in large volumes of data from an ongoing stream of abattoir condemnation records, the use of deep learning to identify lesions in digital images obtained during slaughtering or the mining of free text in electronic health records from veterinary practices for purpose of sentinel surveillance. However, ML is also being applied to tasks that had usually been tackled with traditional statistical data analysis. Statistical models have extensively been used to infer relationships between predictors and disease to inform risk-based surveillance and increasingly, ML algorithms are being used for prediction and forecasting of animal diseases in support of more targeted and efficient surveillance. While ML and inferential statistics can accomplish similar tasks, they have different strengths making one or the other more or less appropriate in a given context.

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