Vial Flavie

Meat inspection data are routinely collected over several years providing the possibility to use historical data for constructing a baseline model defining the expected normal behaviour of the indicator monitored. In countries in which the reporting of data is compulsory (e.g. in the EU), coverage of the majority of the slaughtered population is ensured.

Objective

We evaluate the performance of the improved Farrington algorithm for the detection of simulated outbreaks in meat inspection data.

 

Veterinary syndromic surveillance (VSS) is a fast growing field, but development has been limited by the limited use of standards in recording animal health events and thus their categorization into syndromes. The adoption of syndromic classification standards would allow comparability of outputs from systems using a variety of animal health data sources (clinical data, laboratory tests, slaughterhouse records, rendering plants data, etc), in addition to improving the ability to compare outputs among countries. The project “Standardising Syndromic Classification in Animal Health Data” (SSynCAHD) aims to standardize the classification of animal health records into syndromes.

Objective

To develop an ontology for the classification of animal health data into syndromes with application to syndromic surveillance.

 

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; 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.

Objective

The question of how to aggregate animal health information derived from multiple data streams that vary in their specificity, scale, and behaviour is not trivial. Our view is that outbreak detection in a multivariate context should be viewed as a probabilistic prediction problem.

As interest in One Health (OH) continues to grow, alternative surveillance infrastructure may be needed to support it. Since most population health surveillance is domain specific; as opposed to OH which crosses multiple domains, changes to surveillance infrastructure may be required to optimize OH practice. For change to occur there must be a strong motivation that propagates from a perceived need. Since the purpose of surveillance is to produce information to support decision making, the motivation for change should relate to a lack of surveillance information needed to make OH decisions, or a gap in the surveillance infrastructure required to produce the information.

Objective

The primary purpose of this study was to explore the attitudes of surveillance stakeholders from different domains to:

-determine whether there is a perceived need for OHS

-identify significant surveillance gaps

-assess the motivation to change (fill the gaps)

A secondary purpose was to gather a group of surveillance stakeholders to identify and prioritize strategies to move One Health Surveillance forward.

Taking into account reporting delays in surveillance systems is not methodologically trivial. Consequently, most use the date of the reception of data, rather than the (often unknown) date of the health event itself. The main drawback of this approach is the resulting reduction in sensitivity and specificity1. Combining syndromic data from multiple data streams (most health events may leave a “signature” in multiple data sources) may be performed in a Bayesian framework where the result is presented in the form of a posterior probability for a disease2.

Objective

We apply an empirical Bayesian framework to perform change point analysis on multiple cattle mortality data streams, accounting for delayed reporting of syndromes.