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Abstract In this paper, we explore the benefits that digitalization and Big Data analytics can bring to animal health, emphasizing the need to advance toward precision veterinary epidemiology. This concept takes advantage of multilevel animal health–related data to better understand disease dynamics in a population and design more cost-effective interventions, particularly focusing on livestock health. However, to translate this concept into practice, critical advancements and changes are needed in how we collect, standardize, integrate, share, and use data. Fostering interdisciplinary teams that integrate epidemiologists, veterinarians, and other domain experts with computer scientists, engineers, and data scientists is essential to implement this approach and better address complex animal health issues. 

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 24 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 size 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, 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 emergency of cases in multiple farms, but more work is required to improve the 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.