Integrating Heterogeneous Data for a Multi-disease Outbreak Detection Framework

Ismael Villanueva-Miranda, Monika Akbar

Abstract: Timely detection of an outbreak of any infectious disease is essential to implement timely mitigation strategies. Several approaches have been proposed to model and detect the occurrence of an outbreak. However, many of these approaches rely on health surveillance methods and informal and formal reports of illnesses. This paper proposes a single framework for detecting multiple infectious disease outbreaks by integrating disease-specific domain knowledge and public search trend data. We tested our framework with eleven infectious diseases and compared the results with the Centers for Disease Control and Prevention (CDC) outbreak data. Results show that our framework reaches accuracies greater than 95% in most cases. To our knowledge, this is the first study that uses standardized disease symptoms as main indicators, in combination with web search data, to detect infectious disease outbreaks. This work should pave the path for integrating domain-specific static information with user-generated dynamic data for detecting outbreaks of infectious diseases.