Objective This presentation discusses the problem of detecting small-scale events in biosurveillance data that are relatively sparse in the sense that the median count of monitored time series values is zero. Research goals are to understand conditions when methods adapted for sparseness are warranted, to examine adaptations of control charts and other algorithms under these scenarios, and to compare the detection performance of these algorithms.
Burkom Howard
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. Additionally, for a surveillance system to be most useful, it must adapt to the changing environment in which it operates to accommodate emerging public health events that could not be conceived of when the initial system was developed.
Objective
The objective of this presentation is to describe both within-discipline and across-discipline changes to standard methods and operating procedures that must be adopted to achieve automated systems that will be an effective complement and extension to traditional disease surveillance. This presentation describes adaptations already in place, as well as those still needed to rapidly recognize and respond to public health emergencies.
Numerous recent papers have evaluated algorithms for biosurveillance anomaly detection. Common essential problems in the disparate, evolving data environment include trends, day-of-week effects, and other systematic behavior. Public health monitors have expressed the need for modifiable case definitions, requiring monitoring of time series that cannot be modeled in advance. Thus, automated algorithm selection is required. Recent research showed superior predictive performance of the H-W forecasting method compared to regression based predictors applied to syndromic data. This effort discusses extension to a practical monitoring tool, including selection from parametric and initialization settings based on limited data history, selection criteria for routine updating, specification of confidence limits, and validation of the resulting algorithm.
Objective
The objective is to develop and evaluate an operational alerting algorithm appropriate for the variety of time series behavior observed in biosurveillance data. The Holt-Winters (H-W) implementation of generalized exponential smoothing, comparable to complex regression models in predictive capability and far easier to specify and adapt, is built into a robust detection method.
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.
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 brief data history to derive criteria for algorithm selection.
To compare regression models with the modified C2 algorithm for analysis of time series data and real time outbreak detection.
We evaluated several classifications of emergency department (ED) syndromic data to ascertain best syndrome classifications for ILI.
The statistical process control (SPC) community has developed a wealth of robust, sensitive monitoring methods in the form of control charts [1]. Although such charts have been implemented for a wide variety of health monitoring purposes [2], some implementations monitor data that violate basic assumptions required by the control charts [3] yielding alerting methods with uncertain detection performance. This problem highlights an inherent obstacle to the use of traditional SPC methods for syndromic surveillance: the nature of the data. Syndromic data streams are based not on physical science, as are manufacturing processes, but on changing population behavior and evolving data acquisition and classification procedures. To overcome this obstacle, either more sophisticated detection algorithms must be developed or the data must be preconditioned so that it is appropriate for traditional monitoring tools. Objective: For robust detection performance, alerting algorithms for biosurveillance require input data free of trends, day-of-week effects, and other systematic behavior. Time series forecasting methods may be used to remove this behavior by subtracting forecasts from observations to form residuals for algorithmic input. This abstract examines and compares methods for the automatic preconditioning of health indicator data to enable the timely prospective monitoring required for effective syndromic surveillance.
Objective
For robust detection performance, alerting algorithms for biosurveillance require input data free of trends, day-of-week effects, and other systematic behavior. Time series forecasting methods may be used to remove this behavior by subtracting forecasts from observations to form residuals for algorithmic input. This abstract examines and compares methods for the automatic preconditioning of health indicator data to enable the timely prospective monitoring required for effective syndromic surveillance.
To evaluate four algorithms with varying baseline periods and adjustment for day of week for anomaly detection in syndromic surveillance data.
Concern over oral health-related ED visits stems from the increasing number of unemployed and uninsured, the cost burden of these visits, and the unavailability of indicated dental care in EDs [1]. Of particular interest to NC state public health planners are Medicaid-covered visits. Syndromic data in biosurveillance systems offer a means to quantify these visits overall and by county and age group.
Objective
The objective was to use syndromic surveillance data from the North Carolina Disease Event Tracking and Epidemiologic Collection Tool NCDETECT and from BioSense to quantify the burden on North Carolina (NC) emergency departments of oral health-related visits more appropriate for care in a dental office (ED). Calculations were sought in terms of the Medicaid-covered visit rate relative to the Medicaid-eligible population by age group and by county.
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