Unsupervised clustering of wildlife necropsy data for syndromic surveillance

<p>Abstract</p> <p>Background</p> <p>The importance of wildlife disease surveillance is increasing, because wild animals are playing a growing role as sources of emerging infectious disease events in humans. Syndromic surveillance methods have been developed as a complement to traditional health data analyses, to allow the early detection of unusual health events. Early detection of these events in wildlife could help to protect the health of domestic animals or humans. This paper aims to define syndromes that could be used for the syndromic surveillance of wildlife health data. Wildlife disease monitoring in France, from 1986 onward, has allowed numerous diagnostic data to be collected from wild animals found dead. The authors wanted to identify distinct pathological profiles from these historical data by a global analysis of the registered necropsy descriptions, and discuss how these profiles can be used to define syndromes. In view of the multiplicity and heterogeneity of the available information, the authors suggest constructing syndromic classes by a multivariate statistical analysis and classification procedure grouping cases that share similar pathological characteristics.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Wildlife biosurveillance

The objective of wildlife biosurveillance is to detect health-related events potentially harmful for wild animals, man or domestic animals. Three complementary approaches could be developed to overcome the specific constraints associated with the surveillance of wildlife: monitoring based on a risk analysis, monitoring of sentinel animals and syndromic surveillance. Furthermore, the official notification of pathogens identified through such programmes should be redefined, to encourage countries to exchange information while protecting them against unjustified consequences of such notifications.La biosurveillance de la faune sauvage vise à appliquer à celle-ci une surveillance adaptée qui permettrait de mettre en évidence des phénomènes de santé potentiellement délétères pour celle-ci ou pour la santé de l'Homme ou des animaux domestiques. Compte tenu des contraintes particulières liées à la surveillance de ces animaux, trois approches complémentaires pourraient être développées: une surveillance basée sur une analyse de risque, une surveillance d'animaux sentinelles et une surveillance syndromique. Par ailleurs, la notification officielle d'agents pathogènes découlant de la surveillance sanitaire de la faune sauvage devrait être redéfinie, afin d'encourager l'échange d'informations entre les États, tout en les garantissant contre des conséquences injustifiées d'une telle déclaration

Estimating front-wave velocity of infectious diseases: a simple, efficient method applied to bluetongue

Understanding the spatial dynamics of an infectious disease is critical when attempting to predict where and how fast the disease will spread. We illustrate an approach using a trend-surface analysis (TSA) model combined with a spatial error simultaneous autoregressive model (SARerr model) to estimate the speed of diffusion of bluetongue (BT), an infectious disease of ruminants caused by bluetongue virus (BTV) and transmitted by Culicoides. In a first step to gain further insight into the spatial transmission characteristics of BTV serotype 8, we used 2007-2008 clinical case reports in France and TSA modelling to identify the major directions and speed of disease diffusion. We accounted for spatial autocorrelation by combining TSA with a SARerr model, which led to a trend SARerr model. Overall, BT spread from north-eastern to south-western France. The average trend SARerr-estimated velocity across the country was 5.6 km/day. However, velocities differed between areas and time periods, varying between 2.1 and 9.3 km/day. For more than 83% of the contaminated municipalities, the trend SARerr-estimated velocity was less than 7 km/day. Our study was a first step in describing the diffusion process for BT in France. To our knowledge, it is the first to show that BT spread in France was primarily local and consistent with the active flight of Culicoides and local movements of farm animals. Models such as the trend SARerr models are powerful tools to provide information on direction and speed of disease diffusion when the only data available are date and location of cases