%0 Journal Article
%T Unsupervised clustering of wildlife necropsy data for syndromic surveillance
%A Eva Warns-Petit
%A Eric Morignat
%A Marc Artois
%A Didier Calavas
%J BMC Veterinary Research
%D 2010
%I BioMed Central
%R 10.1186/1746-6148-6-56
%X A three-step procedure was applied: first, a multiple correspondence analysis was performed on necropsy data to reduce them to their principal components. Then hierarchical ascendant clustering was used to partition the data. Finally the k-means algorithm was applied to strengthen the partitioning. Nine clusters were identified: three were species- and disease-specific, three were suggestive of specific pathological conditions but not species-specific, two covered a broader pathological condition and one was miscellaneous. The clusters reflected the most distinct and most frequent disease entities on which the surveillance network focused. They could be used to define distinct syndromes characterised by specific post-mortem findings.The chosen statistical clustering method was found to be a useful tool to retrospectively group cases from our database into distinct and meaningful pathological entities. Syndrome definition from post-mortem findings is potentially useful for early outbreak detection because it uses the earliest available information on disease in wildlife. Furthermore, the proposed typology allows each case to be attributed to a syndrome, thus enabling the exhaustive surveillance of health events through time series analyses.The importance of monitoring wildlife health is increasingly recognised [1,2], because free-ranging wild animals are victims, reservoirs or indicators of an increasing number of disease agents shared with humans and/or domestic animals [3-7].General wildlife disease surveillance is a means of maintaining vigilance against emerging wildlife-related diseases [8,9], but it produces data that are frequently biased [10]. These data are further characterised by the diversity of monitored parameters: species, pathogens, diagnoses, environmental characteristics, etc. The analysis of data from this type of surveillance is usually limited to retrospective descriptive assessments. Passively acquired wildlife accessions may however also give i
%U http://www.biomedcentral.com/1746-6148/6/56