Collaborativehealth: Smart technologies to surveil outbreaks of infectious diseases through direct and indirect citizen participation

Abstract

Early warning systems are essential to mitigate the consequences of outbreaks of infectious diseases, which causes millions of deaths every year. Surveillance systems collect data on epidemic-prone diseases to trigger prompt public health interventions. In recent years, these systems have improved significantly thanks to infodemiology, a recent research field that promotes the use of health-data collected from the Internet. However, early warning systems can be improved regarding the interpretability and confidentiality of the compiled evidences. In addition, they still rely heavily on human intervention to distinguish among confident and non-confident evidences. To solve these concerns we present CollaborativeHealth, an infodemiology platform that compiles evidence from (1) social networks, (2) public reports, and (3) voluntary citizen participation; and makes use of deep-learning technologies to extract knowledge regarding infectious diseases, their symptoms, or poor environment conditions what promote the propagation of these diseases. Finally, all the compiled evidence is available to health-professionals in real-time through a configurable dashboard. The validation of CollaborativeHealth was performed with a real use-case about monitoring infectious disease cases related to Zika, Dengue, Chikungunya, and Influenza in Ecuador.