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Data Visualization

Description

Late in September 2012, the Tennessee Department of Health (TDH) identified a cluster of fungal infections following epidural injection of methylprednisolone acetate (MPA) from a single compounding pharmacy. This presented a public health imperative to contact, educate and monitor approximately 1,100 Tennessee residents who received injections from contaminated MPA lots that were shipped to three clinics in Tennessee. There was no precedent to accomplish this rapidly and efficiently. To accomplish this goal a secure, web-based application designed by TDH for emergency patient management was deployed. 

Objective

The purpose was to facilitate real-time information sharing and data visualization for incident management during the Fungal Infections Outbreak in Tennessee.

Submitted by knowledge_repo… on
Description

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.

Submitted by elamb on
Description

Centre for Health Protection (CHP) plans to conduct a pilot project in developing a syndromic surveillance system using data from Emergency Departments (ED) in Hong Kong. This is part of the Communicable Disease Information System initiative, which aims at enhancing the capability of Hong Kong in the control and prevention of communicable diseases.

 

Objective

This paper describes how the CHP of Hong Kong designed and deployed an online interactive system that uses the data from ED for syndromic surveillance.

Submitted by elamb on
Description

The global health threat of highly pathogenic avian influenza H5N1 has been increasing rapidly in the world since the crosscountry outbreaks during 2003-04. In South and East Asia, the human influenza A (H3N2) was proved to be seeded there with occurring annual cases. Intensive surveillance of influenza is the most urgent strategy to avoid large-scale epidemics and high case fatality rates. Sentinel physicians’ surveillance is the most sensitive mechanism to reflect the health status of community people. In France and Japan, comprehensive sentinel-physician surveillance systems were set up and geographic information system was applied to display the diffusion patterns of influenza-like illness. Kriging method, which was used to display the diffusion, was hard to monitor the multiple temporal and spatial dimensions in one map. Therefore, Ring maps were proposed to overcome this difficulty.

 

Objective

This study describes a visualizing ring maps to monitor the alert levels of Influenza-like illness, and provide possible insights of temporal and spatial diffusion patterns in epidemic and nonepidemic seasons.

Submitted by elamb on
Description

Geographic visualization methods allow analysts to visually discover clusters in multivariate, spatially-referenced data. Computational and statistical cluster detection techniques can automatically detect spatial clusters of high values of a variable of interest. The authors propose that the two approaches can be complementary; and present an integration of the GeoViz Toolkit and Proclude software suites as proof-of-concept.

Submitted by elamb on
Description

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.

Submitted by elamb on

Since 2009, the Cook County Department of Public Health (CCDPH) has created and disseminated weekly surveillance reports to share seasonal influenza data with the community and our healthcare partners. Surveillance data is formatted into tables and graphs using Microsoft Excel, pasted into a Word document, and shared via email listserv and our website in PDF format.

Submitted by Anonymous on
Description

Regional disease surveillance as well as data transparency and sharing are the global trend for mitigating the threat of infectious diseases. The WHO has already played a leading role in FluNet (http:// www.who.int/influenza/gisrs_laboratory/flunet/en/ ) and DenguNet (http://www.who.int/csr/disease/dengue/denguenet/en/). However, the enterovirus-related infections which caused a high disease burden for pre-school children in South-East Asian regions over the last two decades still lack a comprehensive surveillance system in the region [1]. If the spreading pattern and a possible alert mechanism can be identified and set up, it will be beneficial for controlling hand, foot and mouth disease (HFMD) epidemics in East Asia. In some research findings, the transmission of HFMD was correlated with temperature, relative humidity, wind speed, precipitation, population density and the periods in which schools were open [2]. A delayed temporal trend was also found with the increase in latitude [3,4] . In this study, we tried to apply publicly available weekly surveillance data in Japan, Taiwan and Singapore to evaluate the spatio-temporal evolution of HFMD epidemics and how the weather conditions affect the HFMD epidemics.

Objective

Enterovirus epidemics, especially affecting young children, have occurred in South-East Asia every year. If the epidemic periods are inter-correlated among different areas, early warning signals could be issued to prevent or reduce the severity of the later epidemics in other areas. In this study, we integrated the available surveillance and weather data in East Asia to elucidate possible spatio-temporal correlations and weather conditions among different areas from low to high latitude.

Submitted by Magou on
Description

Early detection of a disease outbreak using pre-diagnostic textual data is available in biosurveillance systems with the integration of data such as chief complaints. Social media has been identified as an additional pre-diagnostic data source of interest. Textual data analysis in public health is usually based on a keyword search and often involves a complex Boolean combination of terms that produce results with many false alarms. Epidemiologists may wish to query the data differently based on the event of interest, yet the process is laborious to weed out uninteresting content. Specialized detectors that decide on the topical relevance of keyword search usually require developers to adapt methods to new uses, which is a time- and cost-prohibitive activity. Users need the ability to rapidly build text content detectors on their own.

Objective

To demonstrate a framework for user-customizable text processing that can improve the efficiency and effectiveness of mining text for biosurveillance, with initial application to Twitter.

Submitted by teresa.hamby@d… on