Influenza

An important goal of influenza surveillance is to provide public health decisionmakers with timely estimates of the severity of community-wide influenza. One potential indicator is the number of influenza hospitalizations. In New York City methods for estimating influenza hospitalizations include asking hospitals to self-report, sending field staff to review medical records, and analyzing electronic hospital discharge data available months after influenza season is over. Given the limitations of each of these approaches, we evaluated whether electronic ED data, received daily for syndromic surveillance, could be used to monitor hospitalizations during influenza  epidemics.

Objective

To evaluate whether trends in influenza hospitalizations can be monitored using ED syndromic surveillance data.

The 2003/04 influenza season included a more pathogenetic organism and had an earlier onset. There were noticeably more deaths in otherwise healthy children than in previous seasons. Following this season, States were asked by the Centers for Disease Control and Prevention to increase their surveillance efforts for influenza illness.

Objective 

This paper describes data that was available in Ohio for analysis and considered valuable to determine the occurrence of influenza-like illness (ILI). These data sources were studied to determine their value to ILI surveillance and to develop an improved method of establishing influenza activity levels.

A syndromic surveillance system has been implemented at Kingston, Frontenac and Lennox & Addington Public Health in Kingston Ontario as part of a pilot project funded by the Ontario Ministry of Health and Long-Term Care – Public Health Division. The information captured by the Real-time Outbreak and Disease Surveillance-based syndromic surveillance system includes Febrile Respiratory Illness screening results (implemented since SARS) for Emergency Department (ED) visits and information detailing hospital admissions.

Objective

To use an electronic real-time ED monitoring tool to involve public health, acute care and laboratory stakeholders in an integrated alerting and response process for community-wide influenza.

Though spatio-temporal patterns of influenza spread have often suggested that environmental factors, such as temperature, solar radiation and humidity play a key role, few studies have directly assessed their effect on the timing of annual epidemics. Finkelman et al observed a significant positive relationship between the latitudinal position of temperate countries and epidemic timing. It is hypothesized that during winter months, in temperate regions, decreased skin exposure to sunlight affects immune function by altering the production of certain immunomodulators (e.g. melatonin and Vitamin D3). Other studies have linked temperature and humidity conditions to the rate of transmission of the influenza virus.

Objective 

To assess the strength of the association between peak influenza activity and dew point, average daily temperature, solar radiation, latitude and longitude so that we may better understand the factors that affect virus transmission and/or innate immunity and to determine whether these climate variables should be used as covariates in the surveillance of influenza.

The Public Health Agency of Canada is currently utilizing a syndromic surveillance prototype called the Canadian Early Warning System (CEWS). This system monitors several live data feeds, including emergency room chief complaint records from all seven local hospitals, Telehealth (24/7 nurse hotline) calls, and over-the-counter drug sales from a number of the large chain drug stores. Data trends are analysed for aberrations as early indicators of outbreak events. Collaborators on this Winnipeg, Manitoba-based pilot include the Winnipeg Regional Health Authority and IBM Business Solutions. Algorithms currently in CEWS include the 3, 5 and 7-day moving averages, CUSUM and the CDC’s EARS. We seek to investigate the performance of these algorithms in view of the fact that their detection ability may be dependent upon data source and/or the type of outbreak event.

Objective

To determine the sensitivity, specificity and days to detection of several commonly used algorithms in syndromic surveillance systems.

This presentation introduces the U.S. Department of Homeland Security (DHS) National Bio-Surveillance Integration System (NBIS) and the analytics functionality within the NBIS that integrates and analyzes structured and unstructured data streams across domains to provide inter-agency analysts with an integrated view of threat scenarios. The integration of Human and Animal incidences of Avian Influenza will be used to demonstrate initial capability.

OBJECTIVE This paper describes a series of data mining techniques used to gather and analyze and disseminate large amounts of data from numerous sources in English as well as Chinese. The objective of the analysis is to attempt to identify locations where the data may indicate a current or future outbreak of the A-H5N1 strain of the flu virus.

To measure the impact, within administrative geographic areas, of household income on rates of visits due influenza and RSV among children using real-time syndromic surveillance.

In some influenza seasons, morbidity and mortality closely follow the expected seasonal variation. In these years, approaches such as Serflingís model and seasonal-based syndromic outbreak detectors, in use in EARS, work well. In other years, though, short but intense variations occur in addition to the longer term seasonal variation. These intense outbreaks, which are often multimodal, have important implications for both syndromic surveillance and influenza epidemiology. Unfortunately, they are both difficult to characterize and poorly understood. In this paper, we apply techniques from time-frequency distribution theory to identify the temporal location, duration, and amplitude of intense outbreaks occurring in the presence of longer time scale variations.

A significant amount of resources and effort have been recently invested in syndromic surveillance systems. However, how these systems complement or compare with traditional public health surveillance systems, such as outbreak reporting, is not clear.  

Objective:

The purpose of this paper is to describe the value of outbreak and syndromic surveillance data from the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) syndromic surveillance system to monitor respiratory illness activity in Maryland.