Statistical Approach to Biosurveillance in Crisis: What is Next?

Motivated by the threat of bioterrorism, biosurveillance / syndromic surveillance systems are now in crisis: with the original purpose of early detection, and more than 10 years in existence, no health department has reported using them for this purpose. This has led to a shift away from only early detection of bioterrorist attacks. The goal has been expanded in two directions: firstly, to include both early event detection and situational awareness, so that the focus is not simply on detection, but also on timely response and consequence management; and secondly, to switch the emphasis from bioterrorism only to detecting and responding to natural disease outbreaks such as seasonal and especially pandemic flu. Even with this expansion, early detection capacity is problematic. The reason is uncontrolled alert rates: there is an alarm nearly every day and most health monitors learned to ignore alarms. It results in distrust in statistical methods and in biosurveillance itself. In this presentation, we propose a new approach that has a unique potential to successfully combine capabilities for early event detection (with more effective control of alert rates) and situational awareness including monitoring and predicting outbreaks magnitude, rate of change and duration. No existing biosurveillance systems provide this capability. Our approach is based on epidemiological models, both deterministic and stochastic, and their linear approximations – first-order autoregression models, in combination with a standard statistics toolkit: parameters estimating, confidence intervals constructing and hypotheses testing. The approach is originated from our previous research presented at the recent NESUG and SGF presentations. The proposed simple models provide us with the ability to detect an outbreak and simultaneously predict the timing, size of the epidemic outbreak peak, and also the final proportion of the affected population, which is critical for choosing optimal epidemic control strategies and estimating health resources needed.