Evaluation of Spatial Estimation Methods for Cluster Detection

CDC’s BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Spatial approaches depend strongly on having reliable estimated values for counts among the geographic sub-regions. If estimates are poor, algorithms will find irrelevant clusters, and clusters of importance may be missed.

July 30, 2018

Syndromic Surveillance and Zip Code Data: The Role of Zip Codes in Understanding Populations

Syndromic surveillance systems use residential zip codes for spatial analysis to identify disease clusters. However, the use of emergency medical services can be influenced by geographic proximity, specialty services, and severity of illness. We evaluated zip codes reported to the Boston Public Health Commission’s syndromic surveillance system from 10 Boston emergency departments (EDs).



July 30, 2018

Traveling Epidemic Waves in Biosurveillance Data: Identifying Early Hotspots of Respiratory Illness

We have previously shown that timeliness of detection is influenced both by the data source (e.g., ambulatory vs. emergency department) and demographic characteristics of patient populations (e.g., age). Because epidemic waves are thought to move outward from large cities, patient distance from an urban center also may affect disease susceptibility and hence timing of visits. Here, we describe spatial models of local respiratory illness spread across two major metropolitan areas and identify recurring early hotspots of risk.

July 30, 2018

Visualization of Syndromic Surveillance Using GIS

Syndromic Surveillance has been in use in New York City since 2001, with 2.5 million visits reported from 39 participating emergency departments, covering an estimated 75% of annual visits. As syndromic surveillance becomes increasingly spatial and tied to geography, the resulting spatial analysis is also evolving to provide new methodology and tools. In late 2004, the New York City Department of Health and Mental Hygiene (DOHMH) created the geographic information systems (GIS) Center of Excellence to identify ways in which GIS could enhance programs like syndromic surveillance.

July 30, 2018

Extending Comparisons Beyond Time and Space: Looking for Similarities Between Diseases

Early detection of new diseases such as bovine spongiform encephalopathy is the subject of great interest (Gibbens et al., 2008). Understanding whether a disease is infectious or sporadic becomes essential for the application of control measures. Consistent and robust ways to the assessment of temporal trends are required to help in the elucidation of this question. Clustering of cases in space, or time and space, is also relevant in the understanding of the aetiology of a new disease.

July 30, 2018


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