We implemented the CDC EARS algorithms in our DADAR (Data Analysis, Detection, and Response) situational awareness platform. We encountered some skepticism among some of our partners about the efficacy of these algorithms for more than the simplest tracking of seasonal flu.
We analyzed several flu outbreaks observed in our data, including the H1N1 outbreaks in 2009, and noted that, using the C1 algorithm, even with our adjustable alerting thresholds, there was an uncomfortable number of false alarms in the noisy steady-state data, when the number of reported cases of flu-like symptoms was less than five per day.
We developed an algorithm, RecentMax, that could offer better performance in analyzing our flu data.
Objective
To develop an algorithm for detecting outbreaks of typical transmissible diseases in time series data that offers better sensitivity and specificity than the CDC EARS C1/C2/C3 algorithms while offering much better noise handling performance.