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Held Leonhard

Description

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.

 



 

Submitted by Magou on
Description

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.

Submitted by teresa.hamby@d… on

Surveillance data on various notifiable diseases usually consist of multiple time series of daily, weekly, or monthly counts of new infections. Data are typically reported in several strata defined through administrative geographical areas, gender and/or age groups. Statistical modeling of the resulting multivariate time series is an important task in infectious disease epidemiology. We will discuss time series models - specifically developed for multivariate surveillance count data - that can be used for two distinct roles, understanding and prediction of disease spread.