Event-Based Surveillance (EBS) systems play an important role in the early detection of disease outbreaks by monitoring unstructured data sources. However, they face key challenges, including handling the overwhelming volume of collected articles, detecting false positives, and the lack of explainability in detected events. To address these limitations, we previously developed EpiDCA, an unsupervised model that integrates epidemiological and environmental data, as well as expert-defined parameters, into the classification process. Its initial application to avian influenza (AI) in Asia demonstrated very promising results, comparable to well-known supervised baseline methods. In this paper, we assess the robustness and genericity of EpiDCA by applying it to three different case studies: AI in France, African swine fever (ASF) in Europe, and West Nile virus disease (WND) in Europe. Results showed that EpiDCA effectively distinguishes relevant from irrelevant events, achieving weighted F-scores between 0.64-0.85 across all case studies. Sensitivity analysis demonstrated model robustness, with most parameters showing minimal influence on results. Notably, the incorporation of environmental data and finer spatial granularity significantly improved classification precision. Overall, EpiDCA remains robust and adaptable across diverse epidemiological contexts, further validating its effectiveness as a valuable tool for event-based surveillance, with improved interpretability and real-time classification.
Boudoua et al. (Fri,) studied this question.