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Influenza-Like-Illness (ILI)

Description

To evaluate the potential of using the sales of Over the Counter (OTC) medicines for early detection of infections of public health concern, retrospective analysis of the sales of OTC common cold medications used for influenza-like illness (ILI) has been carried out in Japan since 2003. This presentation assess correlations and predictability of OTC sales to ILI for 2004-05 influenza season and compares with the results from 2003-04 season to discuss on robustness and versatility of OTC sales surveillance.

Submitted by elamb on
Description

Our purpose was to develop an ROC curve for public health surveillance similar to those used in diagnostic testing. We developed syndrome surveillance algorithms with differing sensitivity and specificity in detecting the seasonal influenza (ILI) outbreak. For each algorithm we plotted: days to detect the event against the numbers of false positive alarms during the non-ILI season.

Submitted by elamb on
Description

Disease surveillance provides essential information for control and response planning1. Emergency Room (ER) syndromic surveillance data can help to identify changes in disease incidence and affected group thereby providing valuable additional time for public health interventions1. The current study explored the relationship between ER syndromic surveillance data and influenza notification to the Houston Department of Health and Human services (HDHHS).

Submitted by elamb on
Description

Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition [1]. SSS seek early detection by focusing on pre-diagnostic symptoms that by themselves may not alarm clinicians. We have previously determined the performance of various Case Detector (CD) algorithms at finding cases of influenza-like illness (ILI) recorded in the electronic medical record of the Veterans Administration (VA) health system. In this work, we measure the impact of using CDs of increasing sensitivity but decreasing specificity on the time it takes a VA-based SSS to identify a modeled community-wide influenza outbreak. Objective This work uses a mathematical model of a plausible influenza epidemic to test the influence of different case-detection algorithms on the performance of a real-world syndromic surveillance system (SSS).

Submitted by elamb on
Description

In response to increasing reports of avian influenza being identified throughout the eastern hemisphere, the World Health Organization and the U.S. Department of Health and Human Services have published pandemic influenza preparedness plans. These plans include detailed recommendations for routine influenza surveillance during ongoing interpandemic periods as well as recommendations for enhanced influenza surveillance during episodes of international, national, and local pandemic alerts. Like many states, the Connecticut Department of Public Health (DPH), prepared its own Pandemic Influenza Response Plan. The DPH has also been expanding its arsenal of surveillance systems. These systems include a syndromic surveillance system, known as the Hospital Admissions Surveillance System (HASS), developed in September 2001 to monitor for possible bioterrorism events and emerging infections. HASS data has been utilized to supplement information received from laboratoryconfirmed influenza test, influenza-like-illness reporting, and pneumonia influenza mortality to track seasonal influenza.

 

Objective

This paper examines the results of a review of state pandemic influenza preparedness plans and compares various approaches for routine influenza surveillance during interpandemic periods with approaches for enhanced surveillance during pandemic alerts. The results of this review are compared with the experience of using a hospital-based syndromic surveillance system as a supplement to laboratory and clinical influenza surveillance systems.

Submitted by elamb on
Description

Recognizing the threat of pandemic influenza and new or emerging disease such as SARS, the U.S. Department of Health and Human Services has recommended that schools work in partnership with their local health departments “to develop a surveillance system that would alert the local health department to substantial increases in absenteeism among students.”3 Tarrant County’s pilot project system meets that need and transcends absenteeism data; it seeks to quantify ILI in schools and lets school nurses view daily maps of changing disease patterns, access flu prevention resources, and receive and respond to action items suggested by TCPH. While the focus is on seasonal flu, best practices for mitigating seasonal flu also apply to pandemic flu. Because the system uses open source software4 , it’s affordable and replicable for other public health agencies seeking to strengthen their school partnerships as well as their local or regional biosurveillance capabilities.

Objective

This oral presentation will share key findings and next steps following the first year of a pilot project in which Tarrant County, Texas schools used a Web-based system to share their daily health data with Tarrant County Public Health (TCPH) epidemiologists, who can use ESSENCE1 to analyze the data. The projectís ongoing goal is to reduce the magnitude of flu outbreaks by focusing on school-aged children and youth, where infectious diseases often emerge first and spread rapidly.2

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
Submitted by elamb on
Description

To understand the types of false positive cases identified by an Influenza-like illness (ILI) text classifier by measuring the prevalence of ILI-related concepts that are negated, hypothetical, include explicit mention of temporality, experienced by someone other than the patient, or described in templated text that is difficult to process.

Submitted by elamb on
Description

A significant research topic in biosurveillance is how to group individual events—such as single emergency department (ED) visits and sales of over-thecounter healthcare (OTC) products—into counts of “similar” events. For OTC products, the goal is to find categories of individual products that have superior outbreak detection performance relative to categories that biosurveillance systems currently monitor. We have described a method to identify OTC categories that correlate more highly with disease activity than existing categories.1 However, it is an open question whether a category that correlates more highly—or according to some other model has a higher ‘association’—with disease activity than an existing category necessarily has superior detection performance. Here, we evaluate whether a linear regression procedure that clusters OTC products based on how well they ‘explain’ ED visits for influenzalike illness (ILI) can find categories with superior outbreak-detection performance for influenza.

Objective

To develop a procedure that identifies product categories with superior outbreak detection performance.

Submitted by elamb on