Implementation of a Facility Based County Surveillance System Using Epi Info

Surveillance in nursing homes (Enserink et al., 2011) and day care facilities (Enserink et al., 2012) has been conducted in the Netherlands, but is not commonly practiced in the United States (Buehler et al., 2008). Outbreaks of illnesses within these facilities are required to be reported to the Epidemiology Program, however a small fraction of outbreaks reported come from LTCFs. Without regular communication between LTCFs and the Epidemiology Program, it is likely that many outbreaks are going unreported due to lack of awareness of the reporting requirements by facility staff.

January 25, 2018

Correlation of Tweets Mentioning Influenza Illness and Traditional Surveillance Data

The use of social media as a syndromic sentinel for diseases is an emerging field of growing relevance as the public begins to share more online, particularly in the area of influenza. Several applications have been developed to predict or monitor influenza activity using publicly posted or self-reported online data; however, few have prioritized accuracy at the local level. In 2016, the Cook County Department of Public Health (CCDPH) collected localized Twitter information to evaluate its utility as a potential influenza sentinel data source.

January 21, 2018

Evaluation of Syndrome Algorithms for Detecting Pneumonia Emergency Department Visits

The NYC Department of Health and Mental Hygiene (DOHMH) uses ED syndromic surveillance to monitor near real-time trends in pneumonia visits. The original pneumonia algorithm was developed based on ED chief complaints, and more recently was modified following a legionella outbreak in NYC. In 2016, syndromic data was matched to New York State all payer database (SPARCS) for 2010 through 2015. We leveraged this matched dataset to validate ED visits identified by our pneumonia algorithm and suggest improvements.

January 25, 2018

Enhancing Epidemic Detection Using Syndromic Surveillance and Early Notification Methods

Early Notification Detection Systems have taken a critical role in providing early notice of disease outbreaks. To improve the detection methods for disease outbreaks, many detection methods have been created and implemented. However, there is limited information on the effectively of syndromic surveillance in Thailand. Knowing the performance, strengths and weakness of these surveillance systems in providing early warning for outbreaks will increase disease outbreak detection capacity in Thailand.


January 21, 2018

Epi Evident: Biosurveillance to Monitor, Compare, and Forecast Disease Case Counts

The Epi Evident application was designed for clear and comprehensive visualization for monitoring, comparing, and forecasting notifiable diseases simultaneously across chosen countries. Epi Evident addresses the taxing analytical evaluation of how diseases behave differently across countries. This application provides a user-friendly platform with easily interpretable analytics which allows analysts to conduct biosurveillance with minimal user tasks.

January 25, 2018

Public Health Decisions Using Point of Care Data from Open Source Systems in Africa

Ministries of Health in Low and Middle Income Countries (LMIC) are making or trying to make public health decisions for infectious disease conditions like HIV using data garnered from sentinel events and disease tracking in the community. The process of gathering and aggregating data for these case-based reports for is, in all too often a cumbersome or paper-based process. The Center for Disease Control (CDC) was interested in prototyping and piloting approaches that could improve the efficiency and reliability of case reports in resource-constrained environments.

January 21, 2018

Are the French SAMU data relevant for health surveillance?

The syndromic surveillance SurSaUD® system developed by Sante© publique France, the French National Public Health Agency collects daily data from 4 data sources: emergency departments (OSCOUR® ED network), emergency general practioners (SOS Medecins network), crude mortality (civil status data) and electronic death certification including causes of death. The system aims to timely identify, follow and assess the health impact of unusual or seasonal events on emergency medical activity and mortality.

January 25, 2018

PLOS Disease Surveillance and Forecasting Channel

The PLOS Disease Forecasting and Surveillance Channel debuted on November 28, bringing together two related but distinct research communities – disease forecasting and syndromic surveillance. The Channel features research and commentary from PLOS journals and the broader literature. Explore recent research, projects and related content and follow the Channel for article updates.

March 29, 2018

Delay between Discharge and Admit Time Delay in ADT-A03 messages via LEEDS

The Infectious Disease Epidemiology Section (IDEpi) within the Office of Public Health (LaOPH) conducts syndromic surveillance of emergency departments by means of the Louisiana Early Event Detection System (LEEDS). LEEDS accepts ADT (admit-discharge transfer) messages from participating hospitals, predominately A04 (registration) and A03 (discharge), to obtain symptom or syndrome information on patients reporting to hospital emergency departments.

September 07, 2017

Interpreting specific and general respiratory indicators in syndromic surveillance

Public Health England (PHE) uses syndromic surveillance systems to monitor for seasonal increases in respiratory illness. Respiratory illnesses create a considerable burden on health care services and therefore identifying the timing and intensity of peaks of activity is important for public health decision-making. Furthermore, identifying the incidence of specific respiratory pathogens circulating in the community is essential for targeting public health interventions e.g. vaccination.

July 10, 2017


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