Syndromic surveillance system based on near real-time XXXX mortality monitoring.

@entity291 @entity166 @entity204
UNASSIGNED: Early detection of an @entity166 incursion will minimize the impact of outbreaks in livestock. Syndromic surveillance based on the analysis of readily available data can enhance traditional surveillance systems and allow veterinary authorities to react in a timely manner. This study was based on monitoring the number of @entity291 carcasses sent for rendering in the veterinary unit of Talavera de la Reina (Spain). The aim was to develop a system to detect deviations from expected values which would signal unexpected health events. Historical weekly collected dead @entity291 (WCDC) time series stabilized by the Box-Cox transformation and adjusted by the minimum least squares method were used to build the univariate cycling regression model based on a Fourier transformation. Three different models, according to type of production system, were built to estimate the baseline expected number of WCDC. Two types of risk signals were generated: point risk signals when the observed value was greater than the upper 95% confidence interval of the expected baseline, and cumulative risk signals, generated by a modified cumulative sum algorithm, when the cumulative sums of reported @entity204 were above the cumulative sum of expected @entity204 . Data from 2011 were used to prospectively validate the model generating seven risk signals. None of them were correlated to @entity166 events but some coincided, in time, with very high climatic temperatures recorded in the region. The harvest effect was also observed during the first week of the study year. Establishing appropriate risk signal thresholds is a limiting factor of predictive models; it needs to be adjusted based on experience gained during the use of the models. To increase the sensitivity and specificity of the predictions epidemiological interpretation of non-specific risk signals should be complemented by other sources of information. The methodology developed in this study can enhance other existing early detection surveillance systems. Syndromic surveillance based on mortality monitoring can reduce the detection time for certain disease outbreaks associated with mild mortality only detected at regional level. The methodology can be adapted to monitor other parameters routinely collected at farm level which can be influenced by communicable diseases.
