A0764
Title: Abnormal events detection by a real-time surveillance: The case of influenza outbreaks
Authors: Yao Rao - The University of Liverpool (United Kingdom) [presenting]
Abstract: A surveillance method is introduced by using deviations from probabilistic forecasts by comparing realised observations with probabilistic forecasts. The deviation metric is based on low probability events. Specifically, the problem of syndromic surveillance for influenza (flu) is addressed with the intention of detecting outbreaks, due to new strains of viruses, over and above the normal seasonal pattern. The syndrome is hospital admissions for flu-like illness and hence the data are low counts. In accordance with the count properties of the observations, an integer valued autoregressive process is used to model flu occurrences. Monte Carlo evidence suggests the method works well in stylised but somewhat realistic situations. The model is applied to real flu data and the application indicates that the model estimated on a short run of training data, did not declare false alarms, when used with new observations deemed in control, ex post and the model easily detected the 2009 H1N1 outbreak. Finally, we attempt to predict the timing of the flu season using multi-step ahead probability distribution forecasts.
