Veterinary syndromic surveillance using swine production data for farm health management and early disease detection

Abstract

The use of syndromic surveillance (SyS) has grown in animal health since the 2010s, but the use of production data has been underexplored due to methodological and practical challenges. This paper aimed to tackle some of those challenges by developing a SyS system using production data routinely collected in pig breeding farms. Health-related indicators were created from the recorded data, and two different time-series types emerged: the weekly counts of events traditionally used in SyS; and continuous time-series, where every new event is a new observation, and grouping by time-unit is not applied. Exponentially Weighted Moving Average (EWMA) and Shewhart control charts were used for temporal aberration detection, using three detection limits to create a “severity” score. The system performance was evaluated using simulated outbreaks of porcine respiratory and reproduction syndrome (PRRS) as a disease introduction scenario. The system proved capable of providing early detection of unexpected trends, serving as a useful health and management decision support tool for farmers. Further research is needed to combine results of monitoring multiple parallel time-series into an overall assessment of the risk of reproduction failure.