Identifying High-Risk Areas for Dengue Infection Using Mobility Patterns on Twitter

Traditionally, surveillance systems for dengue and other infectious diseases locate each individual case by home address, aggregate these locations to small areas, and monitor the number of cases in each area over time. However, human mobility plays a key role in dengue transmission, especially due to the mosquito day-biting habit, and relying solely on individuals' residential address as a proxy for dengue infection ignores a multitude of exposures that individuals are subjected to during their daily routines.

June 18, 2019

Developing a Prototype Opioid Surveillance System at a 2-Day Virginia Hackathon

At the Governor’s Opioid Addiction Crisis Datathon in September 2017, a team of Booz Allen data scientists participated in a two-day hackathon to develop a prototype surveillance system for business users to locate areas of high risk across multiple indicators in the State of Virginia. We addressed 1) how different geographic regions experience the opioid overdose epidemic differently by clustering similar counties by socieconomic indicators, and 2) facilitating better data sharing between health care providers and law enforcement.

January 25, 2018

Using Scan Statistic to Detect Heroin Overdose Clusters with Hospital Emergency Room Visit Data

Early detection of heroin overdose clusters is important in the current battle against the opioid crisis to effectively implement prevention and control measures. The New York State syndromic surveillance system collects hospital emergency department (ED) visit data, including visit time, chief complaint, and patient zip code. This data can be used to timely identify potential heroin overdose outbreaks by detecting spatial-temporal case clusters with scan statistic.

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January 25, 2018

Coordinated Enhanced Surveillance with Healthcare Entities for Mass Gathering Events

Mass gatherings can result in morbidity and mortality from communicable and non-communicable diseases, injury, and bioterrorism. Therefore, it is important to identify event-related visits as opposed to community-related visits when conducting public health surveillance. Previous mass gatherings in Virginia have demonstrated the importance of implementing enhanced surveillance to facilitate early detection of public health issues to allow for timelyresponse.

August 21, 2017

Spatio-Temporal Cluster Detection for Legionellosis using Multiple Patient Addresses

The Bureau of Communicable Disease (BCD) at the NYC Department of Health and Mental Hygiene performs daily automated analyses using SaTScan to detect spatio-temporal clusters for 37 reportable diseases. Initially, we analyzed one address per patient, prioritizing home address if available. On September 25, 2015, a BCD investigator noticed two legionellosis cases with similar work addresses. A third case was identified in a nearby residential facility, and an investigation was initiated to identify a common exposure source.

August 10, 2017

Support Vector Subset Scan for Spatial Outbreak Detection

Neill’s fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. The penalized fast subset scan (PFSS)3 provides a flexible framework for adding soft constraints to the fast subset scan, rewarding or penalizing inclusion of individual points into a cluster with additive point-specific penalty terms.

August 10, 2017

Comparing Emergency Department Gunshot Wound Data with Mass Casualty Shooting Reports

Shootings with multiple victims are a concern for public safety and public health. The precise impact of such events and the trends associated with them is dependent on which events are counted. Some reports only consider events with multiple deaths, typically four or more, while other reports also include events with multiple victims and at least one death. Underreporting is also a concern. Some commonly cited databases for these events are based on media reports of shootings which may or may not capture the complete set of events that meet whatever criteria are being considered.

August 15, 2017

Evaluation of Exposure-Type Stratification to Improve Poison Center Surveillance

The Centers for Disease Control and Prevention (CDC) uses the National Poison Data System (NPDS) to conduct surveillance of calls to United States poison centers (PCs) to identify clusters of reports of hazardous exposures and illnesses. NPDS stores basic information from PC calls including call type (information request only or call reporting a possible chemical exposure), exposure agent, demographics, clinical, and other variables.

June 11, 2017

FDA’s tracking and analysis of surveillance sampling isolates for outbreak detection

Identifying, solving, and stopping foodborne outbreaks in the U.S. requires the collaboration and coordination of multiple federal agencies and centers as well as state and local authorities. FDA’s Coordinated Outbreak Response and Evaluation (CORE) Network is responsible for outbreak surveillance, response, and post-response activities related to incidents involving multiple illnesses linked to FDA-regulated food.

June 19, 2017

Improving Detection of Call Clusters through Surveillance of Poison Center Data

The Centers for Disease Control and Prevention (CDC) uses the National Poison Data System (NPDS) to conduct surveillance of calls to United States PCs. PCs provide triage and treatment advice for hazardous exposures through a free national hotline. Information on demographics, health effects, implicated substance(s), medical outcome of the patient, and other variables are collected.

July 06, 2017

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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.

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