The use of R is increasing in the public health disease surveillance community. The ISDS pre-conference workshops and newly formed R Group for Surveillance have been well attended and continue to grow in popularity. The use of R in the National Syndromic Surveillance Program (NSSP) has also been of value to many users who wish to analyze and visualize public health data using custom R scripts.

Syndromic surveillance requires reliable, accurate, and complete healthcare encounter data to assess patterns of illness and respond to public health events. Illinois implemented syndromic surveillance statewide in response to Meaningful Use reporting objectives. To address the need for continuous, automated assessment following initial on-boarding of facility Emergency Department data, we developed an R script to assess the quality of data in the private BioSense locker database.

Booz Allen Hamilton is developing a novel bio-surveillance prototype tool, the Digital Disease Detection Dashboard (D4) to address the questions fundamental to daily biosurveillance analysis and decision making: is something unusual happening (e.g., is an outbreak or novel disease emerging)?, What is the probability that what I’m seeing is by chance?, How confident am I that this data is really detecting a signal?, Why is this happening and can I explain it?; and How many cases should I expect? (e.g., magnitude of event over time).