Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods

Abstract

Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of anomaly detection methods based on machine learning, regression, time series techniques, and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests, and laboratory requests. We introduced synthetic outbreaks of different sizes and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w), and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data, but POD, Se, and POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergence of cases in multiple farms, but more work is required to improve detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases.