Detecting Influenza Outbreaks Based on Spatiotemporal Information from Urban Systems

This paper explores the application of real-time spatial information from urban transport systems to understand the outbreak, severity and spread of seasonal and pandemic influenza outbreaks from a spatiotemporal perspective. We believe that combining travel data with epidemiological data is the first step to develop a tool to predict future epidemics and better understand the effects that these outbreaks have on societal functions over time. Real-time data-streams provide a powerful, yet underutilised tool when it comes to monitoring and detecting changes to the daily behaviour of inhabitants. Historical datasets from public transport and road traffic serves as an initial indication of whether changes in daily transport patterns corresponds to seasonal influenza data. It is expected that changes in daily transportation habits corresponds to swings in daily and weekly influenza activity and that these differences can be measured through geostatistical analysis. Conceptually one could be able to monitor changes in human behaviour and activity in nearly true time by using indicators derived from outside the clinical health services. This type of more up-to-date and geographically precise information could contribute to earlier detection of influenza outbreaks and serve as background for implementing tailor-made emergency response measures over the course of the outbreaks.