Predicted quantitative effect of logistic slaughter on microbial prevalence.

Abstract

Logistic slaughter is an intervention measure intended to reduce cross-contamination during slaughter by slaughtering contaminated units (=(groups of) animals) last. This paper describes a simple mathematical model which predicts the prevalences of contaminated units after logistic and random-order slaughter. The effect of logistic slaughter is the difference between these prevalences. The model assumes that uncontaminated units can become contaminated by contaminated units that were slaughtered before them; the contributions of contaminated units are independent. It also assumes that a slaughterhouse is uncontaminated at the start of the day and that a unit that is contaminated before slaughter also is contaminated after slaughter. The model was analysed using numerical simulations; for a selection of cases, analytical formulas can be derived and are presented. Contamination of broiler flocks with Salmonella was used as a case study. Even for this simple model, data availability is a problem leading to uncertain parameter estimates. An average cross-contamination scenario predicts that the beneficial effect of logistic slaughter is as low as 9.1%, which casts doubt on its usefulness as an intervention measure. The case study produced these general model results: the effect of logistic slaughter increases with the probability of cross-contamination between units; with the length of the slaughter queue; and with sensitivity (the probability of a positive test from a unit contaminated at the start of slaughter). However, the effect is small if the prevalence of contaminated units before slaughter is low or high.