Objective
So as to be able to detect nosocomial outbreaks at an early stage, we examine the construction of a syndromic surveillance system for inpatients who have fever, respiratory symptoms, diarrhea, vomiting or rash and evaluate its statistical properties.
ARIMA models use past values (autoregressive terms) and past forecasting errors (moving average terms) to generate future forecasts, making it a potential candidate method for modeling citywide time series of syndromic data [1]. While past research supports the use of ARIMA modeling as a detection algorithm in syndromic surveillance [2], there has been little evaluation of an ARIMA model's prospective outbreak detection capabilities. We built an ARIMA model to prospectively detect simulated outbreaks in ED syndromic data. This method is one of eight being formally evaluated as part of a grant from the Alfred P. Sloan Foundation.
Objective
To evaluate seasonal autoregressive integrated moving average (ARIMA) models for prospective analysis of New York City (NYC) emergency department (ED) syndromic data.
The goal of adequate biosurveillance is to signal that an outbreak may be occurring and through subsequent work is confirmed or refuted. Such a system should be equally able to detect outbreaks of diseases of extremely low reporting frequency, or those with high seasonality. Methods of detecting increases in notifiable communicable diseases reported to the Missouri Department of Health and Senior Services (MDHSS) were based on quartile comparisons to 5-year historical disease reports for the report week and resulted in frequent detection of statistically significant increases that were, in fact, not indicative of disease outbreaks. Frequently generated alerts led to 'alarm fatigue' in epidemiologists.
Objective
Develop a statistically rigorous automated process for weekly communicable disease report analysis to improve the speed and accuracy of outbreak detection in Missouri.
We previously experimented with tracking influenza in ER chief complaint data using existing syndromic surveillance tools. We identified several deficiencies in these tools: poor natural language processing, inefficient user interfaces, frequent (thus costly) false alarms, and one-size-fits-all approaches to syndromes. Furthermore, we were surprised that some epidemiologists we spoke with had relatively little faith in existing surveillance tools, and so we set out to build one that would address their concerns: DADAR (Data Analysis, Detection, And Response).
Objective
To develop an adaptable platform for periodically loading semi-structured medical text, extracting syndromic information using advanced natural language processing, detecting outbreaks in the data (including the ability to tune sensitivity vs. specificity on a syndrome-by-syndrome basis so as to reduce the rate of false alarms), generating timely cartographic surveillance reports, and providing tools to quickly validate or rule out syndromic alerts.
National telephone health advice service data have been investigated as a source for syndromic surveillance of influenza-like illness and gastroenteritis . Providing a high level of coverage, the system might serve as an early outbreak detection tool. We have previously found that telephone triage service data of acute gastroenteritis was superior to web queries as well as over-the-counter pharmacy sales of anti-diarrhea medication to detect large water- and foodborne outbreaks of gastrointestinal illness in Sweden during the years 2007â2011 (4). However, information is limited regarding the usefulness, characteristics, and signal properties of local telephone triage data for monitoring and identifying outbreaks at the community level.
Objective
Our aim was to use telephone triage data to develop a model for community-level syndromic surveillance that can detect local outbreaks of acute gastroenteritis (AGE) and influenza-like illness (ILI) and allow targeted local disease control information.
As technology advances, the implementation of statistically and computationally intensive methods to detect unusual clusters of illness becomes increasingly feasible at the state and local level [2]. Bayesian methods allow for the incorporation of prior knowledge directly into the model, which could potentially improve estimation of expected counts and enhance outbreak detection. This method is one of eight being formally evaluated as part of a grant from the Alfred P. Sloan Foundation.
Objective
To adapt a previously described Bayesian model-based surveillance technique for cluster detection [1] to NYC Emergency Department (ED) visits.
In Rwanda, communicable diseases are the mostly predominant representing 90% of all reported medical consultations in health centers. The country has often faced epidemics including emerging and re-emerging infectious diseases. To enhance its preparedness to identify and respond to outbreaks and prevent epidemics, the Government of Rwanda has developed and deployed an electronic Integrated Disease Surveillance and Response (eIDSR) working with Voxiva with funding from the U.S. Centers for Disease Control and Prevention(CDC).
Objective:
(1) To describe the implementation of the electronic system for integrated disease surveillance in Rwanda.
(2) To present the sensitivity and specificity of the electronic reporting system to detect potential outbreaks
Space-time scan statistics are often used to identify emerging spatial clusters of disease cases [1,2]. They operate by maximizing a score function (likelihood ratio statistic) over multiple spatio-temporal regions. The temporal component is typically incorporated by aggregating counts across a given time window, thus assuming that the affected region does not change over time. To relax this hard constraint on spatial-temporal “shape” and increase detection power and accuracy when tracking spreading outbreaks, we implement a new graph-based event detection approach which enables identification of dynamic clusters while enforcing temporal consistency constraints between temporally-adjacent spatial regions.
Objective:
We describe a novel graph-based event detection approach which can accurately identify and track dynamic outbreaks (where the affected region changes over time). Our approach enforces soft constraints on temporal consistency, allowing detected regions to grow, shrink, or move while penalizing implausible region dynamics. Using simulated contaminant plumes diffusing through a water distribution system, we demonstrate that our method improves both detection time and spatial-temporal accuracy when tracking dynamic waterborne outbreaks.
The Miami-Dade County Health Department currently utilizes Emergency Department based Syndromic surveillance data, 911 Call Center data, and more recently Public School Absenteeism data. Daily monitoring of school absenteeism data may enhance early outbreak detection in Miami-Dade County in conjunction with the use of other syndromic systems. These systems were employed to detect any possible outbreaks resulting from a large outdoor festival occurring March 11th, 2007. This event had an estimated 1 million visitors and it ended at 7:00 p.m.
Objective
Utility of school absenteeism data to enhance syndromic surveillance activities for unusual public health events or outbreak detection.
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.
Given the strategic significance and national security implications of timely and accurate detection, proper tools for studying and thus improving increasingly complex surveillance algorithms are warranted.
Objective
We describe a method to perform computationally intensive analyses on large volumes of syndromic surveillance data using open-source grid computing technology.