Outbreak Prediction: Aggregating Evidence Through Multivariate Surveillance

Introduction Production animal health syndromic surveillance (PAHSyS) data are varied: there may be standardized ratios, proportions, counts of adverse events, categorical data and even qualitative ‘intelligence’ that may need to be aggregated up a hierarchy. PAHSyS provides some unique challenges for event detection. Livestock populations are made up of many subpopulations which are constantly moving around between farms and markets to slaughter. Pathogen expression often varies across production types and rearing-intensity levels. The complexity of animal production systems necessitates monitoring many time series (Figure 1); and makes the investigation of statistical signals imperative and at the same time difficult and resource intensive. Having multivariate surveillance methods that can work across multiple data streams to increase both sensitivity and specificity are much needed.