Displaying results 9 - 16 of 38
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Data-Adaptive Multivariate Control Charts for Routine Health Monitoring
Content Type: Abstract
This paper investigates the use of data-adaptive multivariate statistical process control (MSPC) charts for outbreak detection using real-world syndromic data. The widely used EARS [1] methods and other adaptive implementations assume implicitly… 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 -
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 -
Resolving the 'Boy Who Cried Wolf' Syndrome
Content Type: Abstract
To date, most syndromic surveillance systems rely heavily on complicated statistical algorithms to identify aberrations. The assumption is that when the statistics identify something unusual, follow-up should occur. However, with multiple strata… read more -
Hybrid Probabilistic Modeling and Automated Data Fusion for Biosurveillance Applications
Content Type: Abstract
The increased threat of bioterrorism and naturally occurring diseases, such as pandemic influenza, continually forces public health authorities to review methods for evaluating data and reports. The objective of bio-surveillance is to automatically… 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 -
Minimizing False Alarms in Syndromic Surveillance
Content Type: Abstract
This paper describes a method of avoiding false alerts in automated syndromic surveillance algorithms which monitor the temporal relationship between a particular monitored syndrome (the ìtargetî) in relationship to other reference healthcare data… read more -
Modeling Disease Surveillance and Assessing its Effectiveness for Detection of Acute Respiratory Outbreaks in Resource-Limited Settings
Content Type: Abstract
A U.S. Department of Defense program is underway to assess health surveillance in resource-poor settings and to evaluate the Early Warning Outbreak Reporting System. This program has included several information-gathering trips,… read more