Bayesian risk assessment for Salmonella in egg laying flocks under zero apparent prevalence and dynamic test sensitivity

Ranta, Jukka; Mikkelä, Antti; Tuominen, Pirkko; Wahlström, Helene

Journal de la société française de statistique, Méthodes statistiques en agronomie, Tome 154 (2013) no. 3, pp. 8-30.

Un modèle à processus de Markov caché à deux états en temps continu a servi à décrire la prévalence de cheptels infectés par les salmonelles durant la phase de ponte dans le cadre de la production d’œufs. L’état infectieux d’un cheptel a été traité comme une variable cachée binaire susceptible d’être détectée comme étant positive aux salmonelles uniquement par des tests microbiologiques imparfaits. La sensibilité du test dépend du type d’échantillonnage et de la méthode d’analyse employés mais aussi de la phase inconnue de l’épidémie parmi les poules du cheptel. Dans un jeu de données issu d’un programme de contrôle national sous prévalence très basse, il est possible que tous les tests à tous les âges génèrent des résultats négatifs. Cependant, une certaine incertitude temporellement variable demeure en regard de la prévalence réelle inconnue, du fait des évolutions temporelles de la sensibilité d’ensemble des tests. En définissant la sensibilité comme une fonction de la durée de l’épidémie au sein du cheptel, un modèle Bayésien a été développé pour l’évaluation quantitative des risques. En employant des hypothèses minimales dérivées de connaissances expertes ou de scénarios plausibles, les effets de la sensibilité des tests changeante au cours du temps ont été pris en compte par intégration sur la durée inconnue de l’infection. Le modèle de sensibilité a été combiné avec le modèle de processus de Markov caché, conditionnellement á la séquence temporelle des résultats de tests. Les calculs ont été effectués avec OpenBUGS.

A continuous time two-state hidden Markov process model was used to describe prevalence of salmonella infected flocks over laying phase in egg production. The infection status of a flock was treated as a binary hidden variable that can be detected as salmonella positive only by imperfect microbiological testing. Sensitivity of the test depends on the sampling type and analysis method used, but also on the unknown phase of epidemic among the hens within the flock. In a data set obtained from a national control programme under very low prevalence, all tests at all ages may show negative results. However, some temporally varying uncertainty remains about the unknown true prevalence, due to temporal changes in overall test sensitivity. By defining the sensitivity as a function of duration of within flock epidemic, Bayesian modeling was developed for quantitative risk assessment. Using minimal assumptions derived from expert knowledge or plausible scenarios, the effect of dynamically changing test sensitivity was accounted for by integration over the unknown time of infection. The sensitivity model was combined with the hidden Markov process model, conditional to temporal sequence of test results. Computations were performed using OpenBUGS.

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