EpiDCA: Adaptation and implementation of a danger theory algorithm for event-based epidemiological surveillance

Boudoua Bahdja, Roche Mathieu, Teisseire Maguelonne, Tran Annelise. 2025. EpiDCA: Adaptation and implementation of a danger theory algorithm for event-based epidemiological surveillance. Computers and Electronics in Agriculture, 229:109693, 9 p.

https://doi.org/10.1016/j.compag.2024.109693

Article de revue ; Article de recherche ; Article de revue à facteur d'impact

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Résumé : Amidst the overwhelming volume of health-related data available, diverse epidemiological surveillance strategies have been adopted to swiftly detect outbreak events. These strategies differ in terms of structure, type, and sources used. When combined, they offer a more comprehensive understanding of epidemiological events then when used alone. In this paper, we propose an unsupervised approach that allows epidemiological data to be combined with risk factors related to disease onset. We applied this method, named EpiDCA, to enhance the classification and early detection capabilities of Event-Based Surveillance (EBS) systems. EpiDCA is an adaptation of the Dendritic Cells Algorithm (DCA) inspired by the danger theory. The DCA has been applied in various studies and has shown promising results in real-time and binary classification problems. However, some stochastic elements in the algorithm, such as the random sampling and the migration threshold in the detection phase, have been criticized. To overcome these limitations, we integrated spatio-temporal information into the method. We then applied EpiDCA to an avian influenza case study and evaluated the results. These were very promising, and comparable to well-known, supervised baseline methods.

Mots-clés Agrovoc : surveillance épidémiologique, épidémiologie, facteur de risque, grippe aviaire, maladie transmise par vecteur

Mots-clés libres : Event-based surveillance, Epidemic intelligence, Danger theory, African swine fever, Avian influenza, One Health

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) MOnitoring Outbreak events for Disease surveillance in a data science context

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