UPDATED: Syndromic Surveillance 101 - Introduction to R for Health Surveillance

In this 26 minute video, Eric Bakota offers an overview of a free statistical package, R, and an overview of commonly used tips and tricks shared in the surveillance community for analysis work in R.

Objectives:

April 30, 2019

Emergency Department Syndromic Surveillance in Michigan: Routine Analysis and Beyond

This presentation shared by Michigan covers the introductory basics of syndromic surveillance. This is a part of a web based training that new users have to complete before getting access to the surveillance system in Michigan. 

May 18, 2018

Virtual Speed Networking with the Analytic Solutions Committee (ASC)

Presented January 11, 2018.

The purpose of the event was to stimulate and facilitate constructive communication and collaboration among analytic method developers and practitioners charged with routine public health surveillance, ranging from disease outbreak surveillance to chronic disease burden assessment and disaster response.

January 11, 2018

Qualitative and Quantitative Predictions of Infectious Diseases in Shirak Marz

The frequency of disease outbreaks varies as a result of complex biological processes. Analysis of these frequencies can reveal patterns that can serve as a basis for predictions.

Objective:

The goal of this study was to identify the periodicity of seven zooanthroponoses in humans, and set epidemic thresholds for future occurrences.

January 21, 2018

Automated Processing of Electronic Data for Disease Surveillance

National initiatives, such as Meaningful Use, are automating the detection and reporting of reportable disease events to public health, which has led to more complete, timely, and accurate public health surveillance data. However, electronic reporting has also lead to significant increases in the number of cases reported to public health. In order for this data to be useful to public health, it must be processed and made available to epidemiologists and investigators in a timely fashion for intervention and monitoring.

January 25, 2018

Revitalizing the Global Public Health Intelligence Network (GPHIN)

The Global Public Health Intelligence Network is a non-traditional all-hazards multilingual surveillance system introduced in 1997 by the Government of Canada in collaboration with the World Health Organization.1 GPHIN software collects news articles, media releases, and incident reports and analyzes them for information about communicable diseases, natural disasters, product recalls, radiological events and other public health crises.

January 25, 2018

Special Event Data

Problem Summary

A truncated historical dataset is provided from one or more subregions with multiple participating hospitals with enough variety in the patient volume and demographics to make the problem challenging and to generate alerting solutions useful to other regions.

October 30, 2017

Metadata Visualization App (MVA) Workgroup

The Metadata Visualization App (MVA) workgroup has been developing a metadata visualization application as part of a proof of concept tool containing jurisdiction specific information on Electronic Health Record (EHR) vendors, EHR  vendor products, aggregate data quality metrics(timeliness, validity and completeness), and facility types participating in syndromic surveillance.

October 31, 2018

Data Quality Committee (DQC)

Our mission as the Data Quality Committee is to engage the NSSP Community of Practice to identify and attempt to address syndromic surveillance data quality challenges with thoughtful discussion and the inclusion of outside stakeholders. We strive to foster relationships between all groups with a hand in syndromic messaging in order to better syndromic surveillance practice for everyone. 

 

October 31, 2018

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Contact Us

National Syndromic
Surveillance Program

Centers for Disease
Control and Prevention

Email:nssp@cdc.gov

The National Syndromic Surveillance Program (NSSP) is a collaboration among states and public health jurisdictions that contribute data to the BioSense Platform, public health practitioners who use local syndromic surveillance systems, CDC programs, other federal agencies, partner organizations, hospitals, healthcare professionals, and academic institutions.

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