ISDS Conference

1) Describe Arizonaís integrated influenza surveillance for school children with a retrospective analysis of data from multiple sources including School-based Syndromic Surveillance Program (SSSP), laboratory-confirmed influenza case reports, sentinel influenza-like illness (ILI) reports, and hospital discharge data. 2) Demonstrate how ILI data collected from SSSP can be integrated into evidence from other data sources to prospectively monitor and detect early increases in influenza among school children.

Objective. This paper describes Project Argus, a novel foreign biological event detection and tracking system.

To construct and validate a prediction algorithm that detects early increases in laboratory reports of enteric illnesses on the basis of calls to a poison control center reporting suspected foodborne illnesses.

Objective Cluster detection with a mechanism for reducing false alarms and increasing sensitivity.

This paper describes a methodology for applying natural language parsing (NLP) technologies, originally developed for analyzing biomedical journal articles, to the monitoring of emergency department patient charts for infectious diseases of interest.

Influenza surveillance provides public health officials and healthcare providers with data on the onset, duration, geographic location, and level of influenza activity in order to guide the local use of interventions. The Influenza Sentinel Provider Surveillance Network tracks influenza-like illness (% ILI) across the U.S. population. Objective: This presentation describes the use of influenza antiviral data from retail pharmacies to supplement influenza surveillance.

To evaluate the robustness of a spatial anonymization algorithm for syndromic surveillance data against a triangulation vulnerability attack. `BACKGROUND We have published an anonymization algorithm that takes precise point locations for patients and moves them a randomized distance according to a 2D Gaussian distribution that is inversely adjusted by the underlying population density. Before such algorithms can be integrated into live systems, assurances are needed so that patients cannot be reidentified through systematic vulnerabilities. Here we investigate the ease with which a spatial anonymization algorithm can be compromised by triangulating the original points with multiple repeated data requests. Obfuscative and cryptographic algorithms may be susceptible to weakening when it is possible for an adversary to produce output from the algorithm according to adversary-provided input. Under this threat model, an adversary could use a syndromic surveillance system to request anonymized patient data from a RHIO or other health network several different times. If the anonymized results are produced each time they are requested, triangulation of original addresses may be possible or the anonymity afforded by the algorithm may be reduced.

We propose a new method for detecting patterns of disease cases that correspond to emerging outbreaks. Our Anomaly Pattern Detector (APD) first uses a "local anomaly detector" to identify individually anomalous records and then searches over subsets of the data to detect self-similar patterns of anomalies.

We report on a retrospective analysis of gastrointestinal syndrome definitions based on chief complaints and ICD9 diagnosis for gastroenteritis during the 2006-07 season of increased norovirus activity.

The paper outlined the major findings and statistical results of syndromic surveillance system in Taiwan since year 2000.