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Disease Monitoring

Description

Effective public health response to emerging infectious diseases, natural disasters, and bioterrorism requires access to real-time, accurate information on disease patterns and healthcare utilization. The ESSENCE surveillance system in use by the Department of Defense (DoD) relies primarily on outpatient clinical impression diagnosis, which accurately characterize broad disease syndromes but may not be sufficient for monitoring specific diseases. DoD outpatient military treatment facilities perform nearly 500,000 microbiology laboratory tests annually. Initiated electronically, the ordered test is recorded immediately; most provide specific results in 24 to 72 hours and may prove useful for monitoring population health. Although a syndrome classification has been developed for laboratory tests, the classification cannot be applied directly to the DoD data and no previous study has validated the use of automated laboratory test orders for syndromic surveillance.

 

Objective

To evaluate the association between military microbiology laboratory test orders and infectious disease patterns.

Submitted by elamb on
Description

Foot-and-mouth disease (FMD) is one of the most devastating diseases of farm animals. There is a critical need for countries to have a global FMD situational awareness. Monitoring the online news sources for FMD-related news is an important component of situational awareness. The FMD Lab at UC Davis (http://fmd.ucdavis.edu/) has developed models and systems for global FMD surveillance, including the FMD BioPortal web-based system jointly with the AI Lab at the University of Arizona. They have also been gathering and processing FMD-related news from the FMD World Reference Laboratory, the OIE, the FAO, among others. However, manual searches are necessary identify and integrate the FMD news into their models and systems. This manual work is not only time consuming and labor intensive but may also lead to the loss of some important information.

 

OBJECTIVE

This paper describes an FMD news monitoring and classification system which can automatically monitor FMD related news from online news data sources, generate news summarization and classify the news into three categories defined by domain experts. The report research is a collaborative effort between the Artificiall Intelligence Lab at the University of Arizona and the FMD Lab at UC Davis.

Submitted by elamb on
Description

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.

Submitted by elamb on
Description

As the summer temperatures soared to their highest ever recorded, Oklahoma experienced its highest disease count ever since the disease had been discovered in New York in 1999. Tulsa County is the second most populous county in Oklahoma and accounted for over one-fourth of the West Nile Cases in Oklahoma. Tulsa City County Health Department is also the only funded mosquito control program in the state that regularly reports to CDC’s AborNet.

 

Objective

Identify, analyze, and summarize WNV in Tulsa County, Oklahoma.

Submitted by hparton on
Description

Reportable disease case data are entered into Merlin by all 67 county health departments in Florida and assigned confirmed, probable, or suspect case status. De-identified reportable disease data from Merlin are sent to ESSENCE-FL once an hour for further analysis and visualization using tools in the surveillance system. These data are available for ad hoc queries, allowing users to monitor disease trends, observe unusual changes in disease activity, and to provide timely situational awareness of emerging events. Based on system algorithms, reportable disease case weekly tallies are assigned an awareness status of increasing intensity from normal to an alert category. These statuses are constantly scrutinized by county and state level epidemiologists to guide disease control efforts in a timely manner, but may not signify definitive actionable information.

 

Objective

In light of recent outbreaks of pertussis, the ability of Florida Department of Health’s (FDOH) Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL) to detect emergent disease outbreaks was examined. Through a partnership with the Johns Hopkins University Applied Physics Laboratory (JHU/APL), FDOH developed a syndromic surveillance system, ESSENCE-FL, with the capacity to monitor reportable disease case data from Merlin, the FDOH Bureau of Epidemiology’s secure webbased reporting and epidemiologic analysis system for reportable diseases. The purpose of this evaluation is to determine the utility and application of ESSENCE-FL system generated disease warnings and alerts originally designed for use with emergency department chief complaint data to reportable disease data to assist in timely detection of outbreaks in promotion of appropriate response and control measures.

Submitted by hparton on
Description

Erysipeloid is a zoonotic bacterial infection transmitted to humans from animals. Symptoms include inflamed joints and skin; there is also a generalized type of the infection in which bacteria spread through the lymphatic and blood vessels, leading to the emergence of widespread skin lesions and the formation of secondary foci of infection in internal organs. Morbidity has no age or gender specifics; there is summer and autumn seasonality. The agent of the infection - Erysipelothrix rhusiopathiae can be found in many domestic and wild animals. Wild rodents and ectoparasites play an essential role in spreading the disease and serve as a source of infection contaminating the environment.

Objective:

The goal of this study was to characterize the epidmiological, geographic, and historical characteristics of erysipeloid outbreaks in the Republic of Armenia.

Submitted by elamb on