Data Mining for the Early Detection of Disease Outbreaks

1. Veterinarian data Over-the-counter medication sales School/Work absenteeism Lab test requests 911 Calls Telephone triage calls Emergency Department records Weng-Keen Wong, School of EECS, Oregon State University Data Mining for the Early Detection of Disease Outbreaks Email: wong@eecs.oregonstate.edu Joint work with the RODS Lab (University of Pittsburgh) and the AUTON Lab (Carnegie Mellon University) Introduction Before public health can respond, we first need to be able to detect that an outbreak is occurring. The earlier we detect the outbreak, the more we can reduce morbidity and mortality. Many cities throughout the US have established syndromic surveillance systems to monitor the health of the community. Syndromic surveillance systems collect and analyze health-related data that precede diagnosis. The threat of a deadly disease outbreak is very real. There are two scenarios of concern: • Naturally occurring outbreaks eg. SARS, Asian bird flu. • Outbreaks due to bioterrorist attacks eg. anthrax, smallpox. Examples of Pre-diagnosis Data The Syndromic Surveillance Pipeline Challenges • Finding anomalies in rich multivariate data that includes spatial, temporal, demographic and symptomatic information. • Finding anomalies that are truly indicative of a disease outbreak of interest. • Combining information from multiple data sources eg. Emergency Department data and over-the-counter medication sales. 1. Identify useful data sources 2. Collect data 3. Analyze data Computer Science comes in here in the form of data mining: find anomalies that correspond to disease outbreaks Data being monitored is HIPAA compliant with personal identifying information removed. The “What’s Strange About Recent Events” (WSARE) Algorithm The Population-wide Anomaly Detection and Assessment (PANDA) Algorithm Find which rules predict unusually high proportions in recent records when compared to the baseline eg. 50/200 records from Baseline have Gender = Male AND Home Location = NW 90/180 records from Recent have Gender = Male AND Home Location = NW Anthrax Release Recent ED records Time Of Release Location of Release … … Female 20-30 50-60 Male Gender Age Decile Age Decile Gender Home Zip Home Zip Other ED Disease Other ED Disease Anthrax Infection Anthrax Infection 15213 15146 Baseline (from a model that takes temporal fluctuations and other factors into account) Respiratory from Anthrax Respiratory CC From Other Respiratory from Anthrax Respiratory CC From Other Respiratory CC Respiratory CC ED Admit from Anthrax ED Admit from Other ED Admit from Anthrax ED Admit from Other Unknown False Respiratory CC When Admitted Respiratory CC When Admitted Yesterday ED Admission never ED Admission Models every individual in the population in order to improve detection of an airborne release of inhalational anthrax