Sum-Product Networks for Early Outbreak Detection of Emerging Diseases
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Recent research in syndromic surveillance has focused primarily on monitoring specific, known diseases, concentrating on a certain clinical picture under surveillance. Outbreaks of emerging infectious diseases with different symptom patterns are likely to be missed by such a surveillance system. In contrast, monitoring all available data for anomalies allows to detect any kind of outbreaks, including infectious diseases with yet unknown syndromic clinical pictures. In this work, we propose to model the joint probability distribution of syndromic data with sum-product networks (SPN), which are able to capture correlations in the monitored data and even allow to consider environmental factors, such as the current influenza infection rate. Conversely to the conventional use of SPNs, we present a new approach to detect anomalies by evaluating p-values on the learned model. Our experiments on synthetic and real data with synthetic outbreaks show that SPNs are able to improve upon state-of-the-art techniques for detecting outbreaks of emerging diseases.
KeywordsSum-product networks Syndromic surveillance Outbreak detection Anomaly detection
We thank our project partners the Health Protection Authority of Frankfurt, the Hesse State Health Office and Centre for Health Protection, the Hesse Ministry of Social Affairs and Integration, the Robert Koch-Institut, the Epias GmbH and the Sana Klinikum Offenbach GmbH who provided insight and expertise that greatly assisted the research. This work was funded by the Innovation Committee of the Federal Joint Committee (G-BA) [ESEG project, grant number 01VSF17034].
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