In July 2021, NSSP announced the release of the Rnssp R package which facilitates access to the ESSENCE system via a secure and simple interface. Rnssp provides methods that streamline the data pull and simplify R code previously required by users to pull data via the APIs using the keyring library. In this tutorial demonstrates how to pull data from the ESSENCE system using Rnssp.
Presented April 22, 2019.
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.
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.
As major disease outbreaks are rare, empirical evaluation of statistical methods for outbreak detection requires the use of modified or completely simulated health event data in addition to real data. Comparisons of different techniques will be more reliable when they are evaluated on the same sets of artificial and real data. To this end, we are developing a toolkit for implementing and evaluating outbreak detection methods and exposing this framework via a web services interface.