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Event-based surveillance (EBS) requires the analysis of an ever-increasing volume of documents, requiring automated processing to support human analysts. Few annotated corpora are available for the evaluation of information extraction tools in the EBS domain. The main objective of this work was to build a corpus containing documents which are representative of those collected in the current EBS information systems, and to annotate them with events and their novelty. We proposed new definitions of infectious events and their novelty suited to the background work of analysts working in the EBS domain, and we compiled a corpus of 305 documents describing 283 infectious events. There were 36 included documents in French, representing a total of 11 events, with the remainder in English. We annotated novelty for the 110 most recent documents in the corpus, resulting in 101 events. The inter-annotator agreement was 0.74 for event identification (F1-Score) and 0.69 [95% CI: 0.51; 0.88] (Kappa) for novelty annotation. The overall agreement for entity annotation was lower, with a significant variation according to the type of entities considered (range 0.30–0.68). This corpus is a useful tool for creating and evaluating algorithms and methods submitted by EBS research teams for event detection and annotation of their novelties, aiming at the operational improvement of document flow processing. The small size of this corpus makes it less suitable for training natural language processing models, although this limitation tends to fade away when using few-shots learning methods. [ABSTRACT FROM AUTHOR]

The objective of this paper is to evaluate a panel of statistical algorithms for temporal outbreak detection. Based on a large dataset of simulated weekly surveillance time series, we performed a systematic assessment of 21 statistical algorithms, 19 implemented in the R package and two other methods. We estimated false positive rate (FPR), probability of detection (POD), probability of detection during the first week, sensitivity, specificity, negative and positive predictive values and F1-measure for each detection method. Then, to identify the factors associated with these performance measures, we ran multivariate Poisson regression models adjusted for the characteristics of the simulated time series (trend, seasonality, dispersion, outbreak sizes, etc.). The FPR ranged from 0.7% to 59.9% and the POD from 43.3% to 88.7%. Some methods had a very high specificity, up to 99.4%, but a low sensitivity. Methods with a high sensitivity (up to 79.5%) had a low specificity. All methods had a high negative predictive value, over 94%, while positive predictive values ranged from 6.5% to 68.4%. Multivariate Poisson regression models showed that performance measures were strongly influenced by the characteristics of time series. Past or current outbreak size and duration strongly influenced detection performances. [ABSTRACT FROM AUTHOR]

Objective: The main objective of this study was to evaluate the contribution of a newly implemented daily surveillance system to the management of the 2009 A(H1N1) influenza pandemic by the military decision-makers at different levels in the French Department of Defence. Methods: The study sample included all medical advisors in the Ministry of Defence and the French Armed Forces Staff and also the members of the specific committee dedicated to flu pandemic control. The variables studied were mental representation of epidemiology, relevance, usefulness, and realtime use of surveillance data using quantitative questionnaires and qualitative face-to-face semistructured interviews. Results: Among the risk managers of the flu pandemic in the Armed Forces, 84% responded. The data generated by epidemiological surveillance were considered relevant and useful, and were reported as effectively used. On the basis of the information produced, concrete actions were planned and implemented in the French Armed Forces. Conclusion: In a pandemic situation involving low mortality, the daily monitoring of the disease did not target public health issues, but it was mainly used to assess the availability of the Armed Forces in real time. For the military staff, epidemiological surveillance represents an essential information tool for the conduct of operations. [ABSTRACT FROM AUTHOR]