Flu Caster: A Pervasive Web Application for High Resolution Situation Assessment and Forecasting of Flu Outbreaks

Recent advances in social media and other platforms have facilitated collection of near real-time surveillance data on flu outbreaks. This data can not only be used for assessing the current extent of flu epidemics in different regions, but also to project the propagation of the disease in the near future. We have developed FluCaster, a pervasive and scalable web application for situation assessment and forecasting of Influenza-like Illness (ILI), commonly referred to as the flu. FluCaster can be used for assessing the prevalence of ILI at highly resolved spatio-temporal levels. Importantly, FluCaster can also provide the user with short and long term forecasts, in the presence of interventions applied on specific sub-populations. FluCaster is comprised of three basic components: (i) a web-enabled user-interface, (ii) a middleware that coordinates interactions between the UI components and the back end models and data store, and (iii) a back end that is comprised of high resolution epidemic simulations, combined with optimization routines for forecasting and situation assessment. The back end also stores and operates on many types of data, including the GIS data consisting of maps and geographic locations, the synthetic population data corresponding to population demographics, and synthetic contact network data for the different regions under consideration. The underlying mathematical models that we have implemented involve highly resolved information on different regional demographics to compute the forecasting output, and form the basis of the forecasting pipeline. We have described the mathematical formulation of the forecasting models in our previous work. Here we describe the overall systems architecture of FluCaster that supports scalability and accessibility for forecasting of flu outbreaks.