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.
In some influenza seasons, morbidity and mortality closely follow the expected seasonal variation. In these years, approaches such as SerflingÃs model and seasonal-based syndromic outbreak detectors, in use in EARS, work well. In other years, though, short but intense variations occur in addition to the longer term seasonal variation. These intense outbreaks, which are often multimodal, have important implications for both syndromic surveillance and influenza epidemiology. Unfortunately, they are both difficult to characterize and poorly understood. In this paper, we apply techniques from time-frequency distribution theory to identify the temporal location, duration, and amplitude of intense outbreaks occurring in the presence of longer time scale variations.
Historical data are essential for development of detection algorithms. Spatio-temporal data, however, are difficult to come by due to variety of issues concerning patient confidentiality. Several approaches have been used to generate benchmark data using statistical methods. Here, we demonstrate how to generate benchmark data using a discrete event model simulating inter- and intra-contact network transmission dynamics of infectious diseases in space and time using publicly available population data.
OBJECTIVE
The objective of this study is to generate benchmark data from a discrete event model simulating the transmission dynamics of an infectious disease within and between contact networks in urban settings using real population data. Such data can be used to test the performance of various temporal and spatio-temporal detection algorithms when real data are scarce or cannot be shared.
A Bayesian Network (BN) is a probabilistic graphical model representing dependencies and relationships. The structure of the network and conditional probabilities capture an expert’s view of a system. BN have been applied to the public health domain for research purposes, but have not been used directly by the end users of public health systems. As BN technology becomes more and more accepted in the public health domain, the data fusion visualization becomes a critical component of the overall system design. The tools developed utilize computer assisted analysis on BN in the public health domain, provide a concise view of the data for better decision support, and shorten the decision making phase allowing rapid dissemination of information to public health.
OBJECTIVE
This paper describes the use and visualization of BNs to better assists public health users. The Data Fusion Visualization (DFV) provides an intuitive graphical interface that supports users in three ways. The first is by providing a seamless drill down interpretation of a dataset. The second is by providing an intuitive interpretation of BN. Finally, by abstracting the visualization from the underlying model, the DFV is capable of masking inter-operating BNs into a single visualization. The DFV provides a graphical representation of BN Network Data Fusion.
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.
Objective
Several authors have described ways to introduce artificial outbreaks into time series for the purpose of developing, testing, and evaluating the effectiveness and timeliness of anomaly detection algorithms, and more generally, early event detection systems. While the statistical anomaly detection methods take into account baseline characteristics of the time series, these simulated outbreaks are introduced on an ad hoc basis and do not take into account those baseline characteristics. Our objective was to develop statistical-based procedures to introduce artificial anomalies into time series, which thus would have wide applicability for evaluation of anomaly detection algorithms against widely different data streams.
The negative effect of air pollution on human health is well documented illustrating increased risk of respiratory, cardiac and other health conditions. Currently, during air pollution episodes Public Health England (PHE) syndromic surveillance systems provide a near real-time analysis of the health impact of poor air quality. In England, syndromic surveillance has previously been used on an ad hoc basis to monitor health impact; this has usually happened during widespread national air pollution episodes where the air pollution index has reached "High" or "Very High" levels on the UK Daily Air Quality Index (DAQI). We now aim to undertake a more systematic approach to understanding the utility of syndromic surveillance for monitoring the health impact of air pollution. This would improve our understanding of the sensitivity and specificity of syndromic surveillance systems for contributing to the public health response to acute air pollution incidents; form a baseline for future interventions; assess whether syndromic surveillance systems provide a useful tool for public health alerting; enable us to explore which pollutants drive changes in health-care seeking behaviour; and add to the knowledge base.
Objective:
To explore the utility of syndromic surveillance systems for detecting and monitoring the impact of air pollution incidents on health-care seeking behaviour in England between 2012 and 2017.