A simulation study on the statistical monitoring of condemnation rates from slaughterhouses for syndromic surveillance: an evaluation based on Swiss data

Abstract

Syndromic surveillance (SyS) systems currently exploit various sources of health-related data, most of which are collected for purposes other than surveillance (e.g. economic). Several European SyS systems use data collected during meat inspection for syndromic surveillance of animal health, as some diseases may be more easily detected post-mortem than at their point of origin or during the ante-mortem inspection upon arrival at the slaughterhouse. In this paper we use simulation to evaluate the performance of a quasi-Poisson regression (also known as an improved Farrington) algorithm for the detection of disease outbreaks during post-mortem inspection of slaughtered animals. When parameterizing the algorithm based on the retrospective analyses of 6 years of historic data, the probability of detection was satisfactory for large (range 83-445 cases) outbreaks but poor for small (range 20-177 cases) outbreaks. Varying the amount of historical data used to fit the algorithm can help increasing the probability of detection for small outbreaks. However, while the use of a 0·975 quantile generated a low false-positive rate, in most cases, more than 50% of outbreak cases had already occurred at the time of detection. High variance observed in the whole carcass condemnations time-series, and lack of flexibility in terms of the temporal distribution of simulated outbreaks resulting from low reporting frequency (monthly), constitute major challenges for early detection of outbreaks in the livestock population based on meat inspection data. Reporting frequency should be increased in the future to improve timeliness of the SyS system while increased sensitivity may be achieved by integrating meat inspection data into a multivariate system simultaneously evaluating multiple sources of data on livestock health.

Keywords: Analysis of data; animal pathogens; emerging infections; modelling; surveillance system.

Fig. 1.

Best retrospective model fit according to the Bayesian Information Criterion for (a) cattle slaughtered under normal conditions, (b) cattle slaughtered under emergency conditions, (c) pigs slaughtered under normal conditions, and (d) pigs slaughtered under emergency conditions.

Fig. 2.

Illustration of outbreak data simulation and inclusion into the time-series.

Fig. 3.

Mean final outbreak size (number of additional condemnations) and duration (in months) over the 1000 simulations for each parameter k. CattleE, cattle slaughtered under emergency conditions; cattleN, cattle slaughtered under normal conditions; pigE, pigs slaughtered under emergency conditions; pigN, pigs slaughtered under normal conditions.

Fig. 4.

Outbreak detection performance of the improved Farrington algorithm with parameter set 1. Probability of detection (POD), false-positive rate (FPR) and percentage of cases until detection (CUD) are averaged over 1000 simulations for each parameter k. The dashed line indicates a POD of 0·5, a FPR of 0·08 that corresponds to one false-positive alarm per year, and a CUD of 50% of outbreak cases, respectively. CattleE, cattle slaughtered under emergency conditions; cattleN, cattle slaughtered under normal conditions; pigE, pigs slaughtered under emergency conditions; pigN, pigs slaughtered under normal conditions.

Fig. 5.

Outbreak detection performance of the improved Farrington algorithm with parameter set 2. Probability of detection (POD), false-positive rate (FPR) and percentage of cases until detection (CUD) are averaged over 1000 simulations for each parameter k. The dashed line indicates a POD of 0·5, a FPR that corresponds to one false-positive alarm per year, and a CUD of 50% of outbreak cases, respectively. CattleE, cattle slaughtered under emergency conditions; cattleN, cattle slaughtered under normal conditions; pigE, pigs slaughtered under emergency conditions; pigN, pigs slaughtered under normal conditions.

Fig. 6.

Outbreak detection performance of the improved Farrington algorithm with parameter set 3. Probability of detection (POD), false-positive rate (FPR) and percentage of cases until detection (CUD) are averaged over 1000 simulations for each parameter k. The dashed line indicates a POD of 0·5, a FPR that corresponds to one false-positive alarm per year, and a CUD of 50% of outbreak cases, respectively. CattleN, cattle slaughtered under normal conditions; pigE, pigs slaughtered under emergency conditions.