Bio-surveillance is an area providing real time or near real time data sets with a rich structure. In this area, the new wave of interest lies in incorporating medical-based data such as percentage of Influenza-Like-Illnesses (ILI) or count of ILI observed during visits to Emergency Room as intelligence function; since many different bioterrorist agents present with flu-like symptoms. Developing a control technique for ILI however is a complex process which involves the unpredictability of the time of emergence of influenza, the severity of the outbreak and the effectiveness of influenza epidemic interventions. Furthermore, the need to detect the beginning of epidemic in an on-line fashion as data are received one at the time and sequentially make the problems surrounding ILI's even more challenging. Statistical tools for analyzing these data are currently well short of being able to capture all their important structural details. Tools from statistical process control are on the face of it ideally suited for the task, since they address the exact problem of detecting a sudden shift against a background of random variability. Bayesian statistical methods are ideally suited to the setting of partial but imperfect information on the statistical parameters describing time series data such as are gathered in BioSense and Sentinel settings.
Objective
This paper presents a Bayesian approach to quality control through the use of sequential update technique in order built a fast detection method for influenza outbreak and potential intentional release of biological agents. The objective is to find evidence of outbreaks against a background in which markers of possible intentional release are non-stationary and serially dependent. This work takes on the US Sentinel ILI data to find this evidence and to address some issues related to the control of infectious diseases. A sensitivity analysis is conducted through simulation to assess timeliness, correct alarm and missed alarm rates of our technique.