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Aberration Detection

Description

This paper describes the issues associated with the creation of a statewide emergency department syndromic surveillance system, part of the South Carolina Aberration Alerting Network (SCAAN), in a predominately rural state.

Submitted by elamb on
Description

Effective anomaly detection depends on the timely, asynchronous generation of anomalies from multiple data streams using multiple algorithms. Our objective is to describe the use of a case manager tool for combining anomalies into cases, and for collaborative investigation and disposition of cases, including data visualization.

Submitted by elamb on
Description

On July 11, 2012, New Jersey Department of Health (DOH) Communicable Disease Service (CDS) surveillance staff received email notification of a statewide anomaly in EpiCenter for Paralysis. Two additional anomalies followed within three hours. Since Paralysis Anomalies are uncommon, staff initiated an investigation to determine if there was an outbreak or other event of concern taking place. Also at question was whether receipt of multiple anomalies in such a short time span was statistically or epidemiologically significant.

Objective

To describe the investigation of a statewide anomaly detected by a newly established state syndromic surveillance system and usage of that system.

Submitted by dbedford on
Description

Public Health England's syndromic surveillance service monitor presentations for gastrointestinal illness to detect increases in health care seeking behaviour driven by infectious gastrointestinal disease. We use regression models to create baselines for expected activity and then identify any periods of signficant increases. The introduction of a rotavirus vaccine in England during July 2013 (Bawa, Z. et al. 2015) led to a reduction in incidence of the disease, requiring a readjustment of baselines.

Objective:

To adjust modelled baselines used for syndromic surveillance to account for public health interventions. Specifically to account for a change in the seasonality of diarrhoea and vomiting indicators following the introduction of a rotavirus vaccine in England.

Submitted by elamb on
Description

Syndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.

Objective:

To investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.

Submitted by elamb on
Description

Since the release of anthrax in October of 2001, there has been increased interest in developing efficient prospective disease surveillance schemes. Poisson CUSUM is a control chart-based method that has been widely used to detect aberrations in disease counts in a single region collected over fixed time intervals. Over the past few years, different methods have been proposed to extend Poisson CUSUM charts to capture the spatial association among several regions simultaneously. In the proposed method, we extend an algorithm in industrial process control using multiple Poisson CUSUM charts to the spatial setting. The spatial association among regions is captured using the method proposed by Raubertas, which has been successfully applied in several prospective surveillance schemes. Also, to improve the power of the traditional multiple Poisson CUSUM charts, Poisson CUSUM charts were used along with fault discovery rate (FDR) control techniques.

Objective

To develop a computationally simple and fast algorithm for rapid detection of outbreaks producing easily interpretable results.

 



 

Submitted by Magou on
Description

Syndromic surveillance generally refers to the monitoring of disease related events, sets of clinical features (i.e. syndromes), or other indicators in a population. Originally conceived as a tool for the early detection of potential bioterrorism outbreaks, syndromic surveillance is also used by health departments as a tool for monitoring seasonal illness, evaluating health interventions, and other health surveillance activities. Over the past decade, the Tennessee Department of Health (TDH) has utilized syndromic surveillance at the jurisdictional level. These standalone, jurisdictional systems utilized chief complaint data from local emergency departments (EDs) and the Early Aberration Reporting System (EARS) developed by CDC. Some jurisdictions integrated other local data for analysis in EARS including 911 call center data, over the counter drug sales, and other non-traditional data sources. The analyses conducted on the data varied from jurisdiction to jurisdiction. CDC dismantled the EARS program in 2011, prompting the need for a complete syndromic surveillance overhaul. TDH decided to implement a centralized, statewide system that would maintain all the capabilities that jurisdictions currently had while allowing for statewide data analysis and aggregation. During this implementation process, TDH has been balancing the short term goal of supporting and maintaining the existing jurisdictional systems while moving forward with acquiring a statewide syndromic surveillance solution and establishing the infrastructure to support it.

Objective

To share lessons learned in Tennessee during its transition from a jurisdictional syndromic surveillance system to a state-wide, centralized system.

 

Submitted by Magou on
Description

The New York City (NYC) syndromic surveillance system has monitored syndromes from NYC emergency department (ED) visits since 2001, using the temporal and spatial scan statistic in SaTScan for aberration detection. Since our syndromic system was initiated, alternative methods have been proposed for outbreak identification. Our goal was to evaluate methods for outbreak detection and apply the best performing method(s) to our daily analysis of syndromic data.

Objective

To evaluate temporal and spatial aberration detection methods for implementation in a local syndromic surveillance system.

Submitted by Magou on
Description

We implemented the CDC EARS algorithms in our DADAR (Data Analysis, Detection, and Response) situational awareness platform. We encountered some skepticism among some of our partners about the efficacy of these algorithms for more than the simplest tracking of seasonal flu.

We analyzed several flu outbreaks observed in our data, including the H1N1 outbreaks in 2009, and noted that, using the C1 algorithm, even with our adjustable alerting thresholds, there was an uncomfortable number of false alarms in the noisy steady-state data, when the number of reported cases of flu-like symptoms was less than five per day.

We developed an algorithm, RecentMax, that could offer better performance in analyzing our flu data.

Objective

To develop an algorithm for detecting outbreaks of typical transmissible diseases in time series data that offers better sensitivity and specificity than the CDC EARS C1/C2/C3 algorithms while offering much better noise handling performance.

Submitted by teresa.hamby@d… on