In the current issue of the BMC: Biomedical Informations and Decision Making journal, researchers at Purdue demonstrate a new algorithm for classifying and analyzing public health data based on a “Seasonal Trend Decomposition using Loess” that allows new insight into time-varying biomedical data. Such an analysis is invaluable for analysing medical emergencies such a bioterrorism or disease outbreaks.
Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods.
A preliminary PDF is available at the link below.
via Abstract | Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts.
