Correlation of Internet APRS Positional Activity Data with CDC Flu Reports

Conventional disease surveillance mechanisms that rely on passive reporting may be too slow and insensitive to rapidly detect a large scale infectious disease outbreak. The reporting time from a patient's initial symptoms to specific disease diagnosis may take days to weeks. To meet this need, new surveillance methods are needed. Referred to as syndromic surveillance, these systems typically rely on prediagnostic data to quickly detect disease outbreaks, such as those caused by bioterrorism. Using data collected from the Internet, and collected data from the Essence II model as well as data from the Center of Disease Control base, we discuss the development, implementation, and evaluation of a time-series syndromic surveillance detection algorithm for influenza like illness (ILI) in Georgia. Automatic Packet Reporting System (APRS) is a real time radio frequency data reporting protocol which contains GPS position and email messages in the form of packets from stationary and mobile stations. This system is employed by licensed amateur radio operators throughout the world with repeaters and digipeaters that collect and transmit the packets to Internet sites. This data is free of normal privacy concerns due to the FCC licensing for Part 97 service. The data stream generates over 100kb/day, has been active for greater than 10 years, and archives are available spanning several years. We introduce a project utilizing this data stream for biosurveillance with a Citizen Health Observer Program (CHOP), where we correlate APRS data to the CDC Prevention health data to establish regional correlation. We mapped the APRS data organized into FCC regions, into CDC reporting regions. With the simple filters applied to the data, there was inconclusive correlation between daily APRS data collected and flu regional reporting. This project will develop more specific internet data filters to detect influenza pandemics alongside bioweapon attacks.