Automating Ambulatory Practice Surveillance for Influenza-Like Illness

Abstract

ILINet data is a central element of influenza surveillance, but data collection is resource-intensive. Increasingly, ambulatory practices are submitting data automatically to syndromic surveillance systems. These syndromic surveillance feeds could potentially provide data to ILINet for a larger number of practices due to the reduced burden on the practices. This work demonstrates that syndromic surveillance data can demonstrate comparable trends to existing ILINet data. However, some allowances in ILI definition need to be made to account for symptom summarization by registrars.