PADI-web: An Event-Based Surveillance System for Detecting, Classifying and Processing Online News

Abstract

The Platform for Automated Extraction of Animal Disease Information from the Web (PADI-web) is a multilingual text mining tool for automatic detection, classification, and extraction of disease outbreak information from online news articles. PADI-web currently monitors the Web for nine animal infectious diseases and eight syndromes in five animal hosts. The classification module is based on a supervised machine learning approach to filter the relevant news with an overall accuracy of 0.94. The classification of relevant news between 5 topic categories (confirmed, suspected or unknown outbreak, preparedness and impact) obtained an overall accuracy of 0.75. In the first six months of its implementation (January–June 2016), PADI-web detected 73% of the outbreaks of African swine fever; 20% of foot-and-mouth disease; 13% of bluetongue, and 62% of highly pathogenic avian influenza. The information extraction module of PADI-web obtained F-scores of 0.80 for locations, 0.85 for dates, 0.95 for diseases, 0.95 for hosts, and 0.85 for case numbers. PADI-web allows complementary disease surveillance in the domain of animal health.