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Spatiotemporal Data

Quantifying the spatial-temporal diffusion of diseases such as seasonal influenza is difficult at the urban scale for a variety of reasons including the low specificity of the extant data, the heterogenous nature of healthcare seeking behavior and the speed with which diseases spread throughout the city. Nevertheless, the New York City Department of Health and Mental Hygiene’s syndromic surveillance system attempts to detect spatial clusters resulting from outbreaks of influenza. The success of such systems is dependent on there being a discernible spatial-temporal pattern of disease at the neighborhood (sub-urban) scale.

We explore ways to extend global methods such as serfling regression that estimate excess burdens during outbreak periods to characterize these patterns. Traditionally, these methods are aggregated at the national or regional scale and are used only to estimate the total burden of a disease outbreak period. Our extension characterizes the spatial-temporal pattern at the neighborhood scale by day. We then compare our characterizations to prospective spatial cluster detection efforts of our syndromic surveillance system and to demographic covariates.

 

Objective

To develop a novel method to characterize the spatial-temporal pattern of seasonal influenza and then use this characterization to: (1) inform the spatial cluster detection efforts of syndromic surveillance, (2) explore the relationship of spatial-temporal patterns and covariates and (3) inform conclusions made about the burden of seasonal and pandemic influenza. 

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Submitted by hparton on

Current influenza-like illness (ILI) monitoring in Idaho is based on syndromic surveillance using laboratory data, combined with periodic person-to-person reports collected by Idaho state workers. This system relies on voluntary reporting.

Electronic medical records offer a method of obtaining data in an automated fashion. The Computerized Patient Record System (CPRS) captures real-time visit information, vital signs, ICD-9, pharmacy, and lab data. The electronic medical record surveillance has been utilized for syndromic surveillance on a regional level. Funds supporting expansion of electronic medical records offer increased ability for use in biosurveillance. The addition of temporo-spatial modeling may improve identification of clusters of cases. This abstract reviews our efforts to develop a real-time system of identifying ILI in Idaho using Veterans Administration data and temporo-spatial techniques.

 

Objective

The objective of this study is to describe initial efforts to establish a real-time syndromic surveillance of ILI in Idaho, using data from the Veterans Administration electronic medical record (CPRS).

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Submitted by hparton on

The Keyhole Markup Language (KML) format has become a recognized standard for the distribution of geographic information system data. In most recent versions of the Real-Time and Outbreak Disease Surveillance (RODS) system, we standardized on KML as our mapping solution. This decision obviates the need for commercial GIS servers and clients, and permits users to easily overlay RODS map output with other websites and software that output KML, for example, EPA, NASA, and NOAA.

We quickly recognized that the mapping tools in RODS have broad applicability in public health and other domains where there is a requirement to display spatial temporal data as it relates to state, county, and zip code geographies. To facilitate these needs, we created the EpiScape map generation service for public use.

 

Objective

This paper describes EpiScape, our map generation service. It generates three-dimensional static or animated maps as KML files that can be used to display epidemiologic data over time and space using Google Earth or Google Maps software.

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Submitted by hparton on

The goal of disease and syndromic surveillance is to monitor and detect aberrations in disease prevalence across space and time. Disease surveillance typically refers to the monitoring of confirmed cases of disease, whereas syndromic surveillance uses syndromes associated with disease to detect aberrations. In either situation, any proper surveillance system should be able to (i) detect, as early as possible, potentially harmful deviations from baseline levels of disease while maintaining low false positive detection rates, (ii) incorporate the spatial and temporal dynamics of a disease system, (iii) be widely applicable to multiple diseases or syndromes, (iv) incorporate covariate information and (v) produce results that are readily interpretable by policy decision makers.

Early approaches to surveillance were primarily computational algorithms. For example, the CUSUM technique and its variants (see, for example, Fricker et al.) monitor the cumulative deviation (over time) of disease counts from some baseline rate. A second line of work uses spatial scan statistics, originally proposed by Kulldorff with later extensions given in Walther and Neill et al.

 

Objective

Syndromic surveillance for new disease outbreaks is an important problem in public health. Many statistical techniques have been devised to address the problem, but none are able to simultaneously achieve important practical goals (good sensitivity and specificity, proper use of domain information, and transparent support to decision-makers). The objective, here, is to improve model-based surveillance methods by (i) detailing the structure of a hierarchical hidden Markov model for the surveillance of disease across space and time and (ii) proposing a new, non-separable spatio-temporal autoregressive model.

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Submitted by hparton on

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.

Submitted by knowledge_repo… on

NC DETECT is the Web-based early event detection and timely public health surveillance system in the North Carolina Public Health Information Network. The reporting system also provides broader public health surveillance reports for emergency department visits related to hurricanes, injuries, asthma,  vaccine-preventable diseases, environmental health and others. NC DETECT receives data on at least a daily basis from four data sources: emergency departments, the statewide poison center, the statewide EMS data collection system, a regional wildlife center and laboratory data from the NC State College of Veterinary Medicine. Data from select urgent care centers are in pilot testing.

 

Objective

Managers of the NC DETECT surveillance system wanted to augment standard tabular Web-based access with a Web-based spatial-temporal interface to allow users to see spatial and temporal characteristics of the surveillance data. Users need to see spatial and temporal patterns in the data to help make decisions about events that require further investigation. The innovative solution using Adobe Flash and Web services to integrate the mapping component with the backend database will be described in this paper.

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Submitted by elamb on

In a classical surveillance system one looks for disturbances in the number of cases, but in a spatio-temporal system, not only the number of cases observed but also where they are located is reported. What location is reported, and to which degree of accuracy it is reported are important. At one extreme les near-perfect information about each case, as with contact tracing; at the other extreme we have no information about location; viz. just that the patient exisits, or a temporal system. From maximum spatial precision to no spatial precision, one gains in speed of reporting and privacy; but one loses power to detect outbreaks. For example, in Ozonoff et al. we see that more than one address is better than just a single one. This general point is intuitively appealing, and can be demonstrated. 

 

Objective

This paper quantifies the effect of not providing full information about the location of patients when dealing with spatio-temporal systems in syndromic surveillance. The study investigates the loss of power to detect clusters when aggregation takes place. 

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Submitted by elamb on

Graph theory concepts are well established in epidemiology, with particular success as a description of agent-based modeling. An agent-based viewpoint leads to conclusions about the spatial distribution of links: infection is more likely among individuals in close proximity. In this analysis, we seek evidence of these temporal-spatial links though the properties of random geometric graphs.

Our investigation begins with the interpoint distance distribution (IDD) approaches referenced, which provide a promising approach to detect outbreaks that are localized in both space and time. Using a Mahalanobis-based metric, this distribution is compared to an expected distribution derived from historical records.

Unfortunately, when applied to a complex data set such as from Children’s Hospital Boston, the IDD provides inadequate power. Emergency Department chief complaints from 1/1/2000-12/31/2004 were used to identify patients with infectious respiratory illness based on a triage process.

As in most realistic catchments, the historic density of patients varies greatly over the catchment area.

 

Objective

This paper uses geometric random graph concepts to develop early detection algorithms for the real-time detection and localization of outbreaks.

Referenced File
Submitted by elamb on

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.

Referenced File
Submitted by elamb on

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.

Referenced File
Submitted by elamb on
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