Enabling Near Real-time Surveillance of Influenza-like Illness

Abstract

Traditional public health surveillance systems would benefit from near real-time data integration and visualization that combines information from traditional and internet sources. In this paper, we describe a prototype system implemented to better automate the process of data collection, analysis and visualization of Influenza-Like Illness surveillance data. This approach enables timelier responses to abnormal events such as clusters, outbreaks and trends.