Influenza Surveillance Using Wearable Mobile Health Devices

Influenza surveillance has been a major focus of Data Science efforts to use novel data sources in population and public health. This interest reflects the public health utility of timely identification of flu outbreaks and characterization of their severity and dynamics. Such information can inform mitigation efforts including the targeting of interventions and public health messaging. The key requirement for influenza surveillance systems based on novel data streams is establishing their relationship with underlying influenza patterns.

June 18, 2019

Linking Emergency Medical Service Data to Death Records for Opioid Mortality Surveillance

Opioid abuse has increased exponentially in recent years throughout the United States, leading to an increase in the incidence of emergency response activities, hospitalization, and mortality related to opioid overdose.

June 18, 2019

Strengthening Health Surveillance through the Development of Interagency Relationships

Introduction: response to this, the Centers for Disease Control and Prevention (CDC), CSTE, and the American Association of Poison Control Centers (AAPCC) members created the Poison Center Public Health Community of Practice (CoP). The CoP acts as a platform, to facilitate sharing experiences, identify best practices, and develop relationships among federal agencies, state and local health departments (HD), and PCs.

June 18, 2019

A Semantic Platform for Surveillance of Adverse Childhood Experiences

Adverse Childhood Experiences (ACEs) have been linked to a variety of detrimental health and social outcomes. In the last 20 years, the association between ACEs with several adult health risk behaviors, conditions, and diseases including suicides, and substance abuse, mental health disturbances and impaired memory, nervous, endocrine and immune systems impairments, and criminal activities have been studied. One of the challenges in studying and timely diagnosis of ACEs is that the links between specific childhood experiences and their health outcomes are not totally clear.

June 18, 2019

Human-learned lessons about machine learning in public health surveillance

Presented December 13, 2018.

For public health surveillance, is machine learning worth the effort? What methods are relevant? Do you need special hardware? This talk was motivated by these and other questions asked by ISDS members. It will focus on providing practical—and slightly opinionated—advice about how to determine whether machine learning could be a useful tool for your problem.

Presenter

December 21, 2018

Tourism and Health Information System (THiS) in the Caribbean, June-September 2017

Travel and tourism pose global health security risks via the introduction and spread of disease, as demonstrated by the H1N1 pandemic (2009), Chikungunya (2013), and recent Zika virus outbreak. In 2016, nearly 60 million persons visited the Caribbean. Historically no regional surveillance systems for illnesses in visitor populations existed.

January 19, 2018

Surveillance for Mass Gatherings: NCAA Final Four 2017 in Maricopa County, Arizona

Final Four-associated events culminated in four days of intense activity from March 31st through April 3rd, and added an estimated 400,000 visitors to Maricopa County's 4.2 million residents.

Objective:

To describe and present results for the enhanced epidemiologic surveillance system established during the 2017 National Collegiate Athletic Association Division I Men’s College Basketball Championship (Final Four) events.

January 21, 2018

Field Team Syndromic Surveillance for Mass Gatherings: NCAA Final Four 2017

Final Four-associated events culminated in four days of intense activity from 3/31/17-4/3/17, which attracted an estimated 400,000 visitors to Maricopa County (population 4.2 million). Field teams of staff and volunteers were deployed to three days of Music Fest, four days of Fan Fest, and three Final Four games (Games) as part of an enhanced epidemiologic surveillance system.

Objective:

January 21, 2018

Barriers and facilitators of reporting foodborne illness

Traditional surveillance methods have a major challenge to estimating the burden of disease due to underreporting. Participatory surveillance techniques can help supplement to monitor and detect foodborne outbreaks while reducing the impact of underreporting. As there is a low participation rate in Singapore, this study aims to better understand the barriers and facilitators to reporting and assesses what improvements can increase participation.

Objective:

January 21, 2018

Evaluation of approaches that adjust for biases in participatory surveillance systems

Because the dynamics and severity of influenza in the US vary each season, yearly estimates of disease burden in the population are essential to evaluate interventions and allocate resources. The CDC uses data from a national health-care based surveillance system and mathematical models to estimate the overall burden of disease in the general population. Over the past decade, crowd-sourced syndromic surveillance systems have emerged as a digital data source that collects health-related information in near real-time.

January 21, 2018

Pages

Contact Us

National Syndromic
Surveillance Program

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, Center for Disease Control and Prevention programs, other federal agencies, partner organizations, hospitals, healthcare professionals, and academic institutions.

Site created by Fusani Applications