An Evaluation of Wavelet-Based Techniques for Prediction and Anomaly Detection in Univariate Syndromic Data

While several authors have advocated wavelets for biosurveillance, there are few published wavelet method evaluations using real syndromic data. Goldenberg et al. performed an analysis using wavelet predictions as a way of detecting a simulated anthrax outbreak. The commercial RODS application uses averaged wavelet levels to normalize for longterm trends and negative singularities.

July 30, 2018

Data-Adaptive Multivariate Control Charts for Routine Health Monitoring

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 that nonsta-tionarity and/or the lack of historic data preclude the conventional Phase I/Phase II approach of SPC. This work examines that assumption formally by evaluating and comparing the false alarm rates and sensitivity of adaptive and non-adaptive MSPC charts applied to simulated outbreaks injected into both desea-sonalized and raw data.

July 30, 2018

Modeling Clinician Detection Time of a Disease Outbreak Due to Inhalational Anthrax

We developed a probabilistic model of how clinicians are expected to detect a disease outbreak due to an outdoor release of anthrax spores, when the clinicians only have access to traditional clinical information (e.g., no computer-based alerts). We used this model to estimate an upper bound on the amount of time expected for clinicians to detect such an outbreak. Such estimates may be useful in planning for outbreaks and in assessing the usefulness of various computer-based outbreak detection algorithms.

July 30, 2018

Using Age as Space: Looking for Citywide Age Clusters of Influenza

There has been much recent interest in using disease signatures to better recognize disease outbreaks. Conversely, the metrics used to describe these signatures can also be used to better characterize the outbreaks. Recent work at the New York City Department of Health has shown the ability to identify characteristic age-specific patterns during influenza outbreaks. One issue that remains is how to implement a search for such patterns using prospective outbreak detection tools such as SatScan.

July 30, 2018

An Empirical Comparison of Spatial Scan Statistics for Outbreak Detection

Expectation-based scan statistics extend the traditional spatial scan statistic approach by using historical data to infer the expected counts for each spatial location, then detecting regions with higher than expected counts. Here we consider five recently proposed expectation-based statistics: the expectation-based Poisson (EBP), expectation-based Gaussian (EBG), population-based Poisson (PBP), populationbased Gaussian (PBG), and robust Bernoulli-Poisson (RBP) methods.

September 20, 2018

A Novel, Context-Sensitive Approach to Anonymizing Spatial Surveillance Data: Impact on Outbreak Detection

The use of spatially-based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health datasets, by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of blurring patient locations on detection of spatial clustering as measured by the SaTScan purely-spatial Bernoulli scanning statistic.

July 30, 2018

Using Open-Source Grid-Computing Technology to Improve Processing Time for Geospatial Syndromic Surveillance Data

Outbreak detection algorithms for syndromic surveillance data are becoming increasingly complex. Initial algorithms focused on temporal data but newer methods incorporate geospatial dimensions. As methods evolve, it is important to understand the effects on detection of both algorithm parameters and population characteristics. Intensive, iterative data analyses are required to accomplish this. Even with leading-edge computer hardware, it can take weeks or months to complete analyses using advanced signal detection techniques such as the space-time scan statistic in the SaTScan program.

July 30, 2018


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