Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study (Preprint)

Abstract

Background Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one-to three-week delay. Accurate real-time monitoring systems of influenza outbreaks could be useful for public health decisions. Several works have investigated the possibility to use internet-users' activity data and different statistical models to predict influenza epidemics in near real-time. However, very few studies have investigated hospital big data. Objective Here, we compared internet and electronic health records (EHR) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods We used Google Data for internet data and the clinical data warehouse eHOP, that includes all EHR from Rennes University Hospital (France), for hospital data. We compared three statistical models, Random Forest (RF), Elastic Net, and Support Vector Machine (SVM).