UNASSIGNED: We performed a simulation study to evaluate the performances of an @entity154 detection algorithm considered in the frame of an automated surveillance system of @entity291 mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of @entity204 in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra @entity204 in the French @entity291 population (about 200,000 herds and 20 million @entity291 ) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra @entity204 in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of @entity204 considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra @entity204 in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated @entity204 and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal @entity291 mortality increases possibly related to unidentified health events.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in XXXX Mortality Records.

@entity154 :: ('MESH:D000013', 'Disease') :: ['anomaly'] @entity204 :: ('MESH:D003643', 'Disease') :: ['deaths'] @entity291 :: ('9913', 'Species') :: ['cattle']
@entity291 :: (9913,Species) :: ['Cattle']
