Visualization

Numerous methods have been applied to the problem of modeling temporal properties of disease surveillance data; the ESSENCE system contains a widely used approach (1). STL (2) is a flexible, wellproven method for temporal modeling that decomposes the series into frequency components. A periodic component like DW can be exactly periodic or evolve through time. STL is based on loess (3), which can model a numeric response as a function of any explanatory variables. After the STL modeling of the counts, we will add patient address and produce a timespace modeling using both STL and more general loess methods.

Objective

Use the STL local-regression (loess) decomposition procedure and transformation to model the univariate time-series characteristics of chief-complaint daily counts as a first step in a time and spatial modeling. Develop visualization tools for model display and checking.

As the Georgia Division of Public Health began constructing a systems interface for its syndromic surveillance program, the nature and intended use of these data inspired new approaches to interface design. With the temporal and spatial components of these data serving as fundamental determinants within common aberration detection methods (e.g., Early Aberration Reporting System, SaTScan™), it became apparent that an interface technique that could present a synthesis of the two might better facilitate the visualization, interpretation and analysis of these data.

Typical presentations of data spatially oriented at the zip code level use a color gradient applied to a zip code polygon to represent the differences in magnitude of events within a given region across a particular time span. Typical presentations of temporally oriented data use time series graphs and tabular formats. Visualizations that present both aspects of spatially and temporally rich datasets within a single visualization are noticeably absent.

Objective

This paper describes an approach to the visualization of disease surveillance data through the use of animation techniques applied to datasets with both temporal and geospatial components.

Infectious disease outbreaks require rapid access to information to support a coordinated response from healthcare providers and public health officials. They need to know the size, spread, and location of the outbreak, and they also need access to models that will help them to determine the best strategy to contain the outbreak. 

There are numerous software tools for outbreak detection, and there are also surveillance systems that depend on communication between health care professionals. Most of those systems use a single type of surveillance data (e.g., syndromic, mandatory reporting, or laboratory) and focus on human surveillance.

However, there are fewer options for planning responses to outbreaks. Modeling and simulation are complex and resource-intensive. For example, EpiSims and EpiCast, developed by the National Institute of Health Models of Infectious Disease Agent Study involve large, diverse datasets and require access to high-performance computing.

Cyberenvironments are an integrated set of tools and services tailored to a specific discipline that allows the community to leverage the national cyberinfrastructure in their research and teaching. They provide data stores, computational capabilities, analysis and visualization services, and interfaces to shared instruments and sensor networks.

The National Center for Supercomputing Applications is applying the concept of cyberenvironments to infectious disease surveillance to produce INDICATOR.

Objective

This paper describes INDICATOR, a biosurveillance cyberenvironment used to analyze hospital data and generate alerts for unusual values.

New York City ED syndromic surveillance data uses SaTScan to detect spatial signals. SaTScan analysis has been integrated into SAS since 2002, and signal maps have been generated from SAS since 2003. Signal maps are created occasionally to investigate a severe outbreak based on the SaTScan results. Previous use and integration of additional GIS analysis in ArcGIS has been done manually, requiring more time, and running the risk of being less consistent than an automated method. This script now integrates the SAS, SaTScan and spatial analysis from ArcGIS to create high-quality maps in an automated procedure.

Objective

The objective was to minimize the amount of time spent on routine, daily analysis of syndromic data, integrate additional spatial analysis, create better maps, and cut response times to outbreaks.

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.

Scan statistics is one of the most widely used method for detecting and locating the clusters of disease or health-related events through the space-time dimension. Although the Specific software of SatScan is available for free and easier to use graphical user interface (GUI) interface, the click way and the resulting text format have became obstacles in biosurveillance since automated or reproduction operation and the fast communicate information tool appeared. With the power of R software and rsatscan package, we extended the visualization results to become a faster, more effective communication and motivation tool.

Objective:

The purpose of this article was to provide static and interact mapping for the results' SaTscan with R package thereby reduce the gap between decision-makers and researchers.