Utilising pathology codes to detect increases in aquatic animal disease occurrence: a retrospective case study

摘要

A case study utilising an alert-generating algorithm to detect increased disease occurrence in aquatic animals is described. The algorithm comprises a Generalized Linear Model to identify anomalous high occurrences and/or increasing trends over time. Its utility is explored with a retrospective analysis of summary pathology codes associated with the emergence of Amoebic Gill Disease in Scotland. The algorithm detects moderate to large increases in occurrence emphasising the continuing role of pathologists in identifying unexpected disease events.