Displaying results 1 - 8 of 75
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A Value-Driven Framework For The Evaluation Of Biosurveillance Systems
Content Type: Abstract
Evaluation and strengthening of biosurveillance systems is acomplex process that involves sequential decision steps, numerous stakeholders, and requires accommodating multiple and conflicting objectives. Biosurveillance evaluation, the initiating… read more -
Development and Evaluation of a Data-adaptive Algorithm for Univariate Temporal Biosurveillance Data
Content Type: Abstract
Numerous recent papers have evaluated algorithms for biosurveillance anomaly detection. Common essential problems in the disparate, evolving data environment include trends, day-of-week effects, and other systematic behavior.… read more -
Essential Requirements for Effective Advanced Disease Surveillance
Content Type: Abstract
Advanced surveillance systems require expertise from the fields of medicine, epidemiology, biostatistics, and information technology to develop a surveillance application that will automatically acquire, archive, process and present data to the user… read more -
Evaluation of Alerting Sparse-Data Streams of Population Healthcare-Seeking Data
Content Type: Abstract
Objective This presentation discusses the problem of detecting small-scale events in biosurveillance data that are relatively sparse in the sense that the median count of monitored time series values is zero. Research goals are to understand… read more -
Evaluation of Spatial Estimation Methods for Cluster Detection
Content Type: Abstract
CDC’s BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of… read more -
Syndromic Prediction Power: Comparing Covariates and Baselines
Content Type: Abstract
The eleven syndrome classifications for clinical data records monitored by BioSense include rare events such as death or lymphadenitis and also common occurrences such as respiratory infections. BioSense currently uses two statistical methods for… read more -
Performance Characteristics of Control Chart Detection Methods
Content Type: Abstract
To recognize outbreaks so that early interventions can be applied, BioSense uses a modification of the EARS C2 method, stratifying days used to calculate the expected value by weekend vs weekday, and including a rate-based method… read more -
Automated Time Series Forecasting for Biosurveillance
Content Type: Abstract
The statistical process control (SPC) community has developed a wealth of robust, sensitive monitoring methods in the form of control charts [1]. Although such charts have been implemented for a wide variety of health monitoring purposes [2], some… read more