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Lindberg Ann

A review of the development of veterinary syndromic surveillance in 2011 indicated that the field was incipient, but fast growing. Many countries are starting to explore different sources of data for syndromic surveillance. Some of the data streams evaluated share similarities with those used in public health syndromic surveillance, such as clinical records and laboratory data. However, many unique animal data sources have arisen, such as abattoir and carcass collection data. We suggest there are three main challenges in the current development of animal syndromic surveillance: The lack of standards in disease classification; The development of statistical methods appropriate to deal with animal data; The creation of ready-to-use tools that employ these statistical methods.

Objective

To summarize the challenges in the development of syndromic surveillance tools in veterinary medicine, and describe the development of an R package to address some of the current gaps.

Submitted by knowledge_repo… on

To describe an R package that was designed to provide ready implementation of veterinary syndromic surveillance systems, from classified data to the generation of alerts and an html interface.

Introduction

Introduction

The field of veterinary syndromic surveillance (VSS) is developing fast, with countries exploring a great variety of data sources. After implementing two VSS systems we have demonstrated that the steps from classified data to full system implementation can be streamlined, and published a guideline for implementation. All the steps described have been made available in an R package (https:// github.com/nandadorea/vetsyn). We aim to demonstrate the utility and potential of this streamlined approach.

 

Submitted by aising on

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.

 

Submitted by Magou on
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