Displaying results 9 - 16 of 75
-
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 -
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 -
Modifications to Spatial Scan Statistics for Estimated Probabilities at Fine-Resolution in Highly Skewed Spatial Distributions
Content Type: Abstract
Estimation of representative spatial probabilities and expected counts from baseline data can cause problems in applying spatial scan statistics when observed events are sparse in a large percentage of the spatial zones (e.g.,… read more -
A Pilot Study of Aberration Detection Algorithms with Simulated Data
Content Type: Abstract
To evaluate four algorithms with varying baseline periods and adjustment for day of week for anomaly detection in syndromic surveillance data. read more -
Classification of Emergency Department Syndromic Data for Seasonal Influenza Surveillance
Content Type: Abstract
We evaluated several classifications of emergency department (ED) syndromic data to ascertain best syndrome classifications for ILI. -
Comparison of Regression Models with Modified Time Series Methods for BioSurveillance
Content Type: Abstract
To compare regression models with the modified C2 algorithm for analysis of time series data and real time outbreak detection. -
Data-Adapted Temporal Alerting Algorithms for Routine Health Monitoring
Content Type: Abstract
This paper discusses selection of temporal alerting algorithms for syndromic surveillance to achieve reliable detection performance based on statistical properties and the epidemiological context of the input data. We used quantities calculated from… read more