BioSurveillance

To immediately monitor disease outbreaks, the application of laboratory-based surveillance is more popular in recent years. Taiwan Centers for Disease Control (TCDC) has developed LARS to collect the laboratory-confirmed cases caused by any of 20 pathogens daily via automated submitting of reports from hospital laboratory information system (LIS) to LARS since 2014 [1]. LOINC is used as standardized format for messaging inspection data [1, 2]. There are 37 hospitals have joined LARS, coverage rate about 59% of all hospitals in Taiwan. Recently, more than 10,000 of data are collected weekly and used in monitoring pathogen activity [3]. Therefore, it is important to ensure data quality that the data will lead to valuable information for public health surveillance.

Objective

To improve data quality and sustain a good quality data collected by Laboratory Automated Reporting System (LARS), we use a Threestage Data Quality Correction (3DQC) strategy to ensure data accuracy.

The DoD provides daily outpatient and emergency room data feeds to the BioSense Platform within NSSP, maintained by the Centers for Disease Control and Prevention. This data includes demographic characteristics and diagnosis codes for health encounter visits of Military Health System beneficiaries, including active duty, active duty family members, retirees, and retiree family members. NSSP functions through collaboration with local, state, and federal public health partners utilizing the BioSense Platform, an electronic health information system.

Objective

The Department of Defense data is available to National Syndromic Surveillance Program (NSSP) users to conduct syndromic surveillance. This report summarizes the demographic characteristics of DoD health encounter visits.

The Biosurveillance Ecosystem (BSVE) is a biological and chemical threat surveillance system sponsored by the Defense Threat Reduction Agency (DTRA). BSVE is intended to be user-friendly, multi-agency, cooperative, modular and threat agnostic platform for biosurveillance [2]. In BSVE, a web-based workbench presents the analyst with applications (apps) developed by various DTRAfunded researchers, which are deployed on-demand in the cloud (e.g., Amazon Web Services). These apps aim to address emerging needs and refine capabilities to enable early warning of chemical and biological threats for multiple users across local, state, and federal agencies. Soda Pop is an app developed by Pacific Northwest National Laboratory (PNNL) to meet the current needs of the BSVE for early warning and detection of disease outbreaks. Aimed for use by a diverse set of analysts, the application is agnostic to data source and spatial scale enabling it to be generalizable across many diseases and locations. To achieve this, we placed a particular emphasis on clustering and alerting of disease signals within Soda Pop without strong prior assumptions on the nature of observed diseased counts.

Objective

To introduce Soda Pop, an R/Shiny application designed to be a disease agnostic time-series clustering, alarming, and forecasting tool to assist in disease surveillance “triage, analysis and reporting” workflows within the Biosurveillance Ecosystem (BSVE). In this poster, we highlight the new capabilities that are brought to the BSVE by Soda Pop with an emphasis on the impact of metholodogical decisions.

ARIs have epidemic and pandemic potential. Prediction of presence of ARIs from individual signs and symptoms in existing studies have been based on clinically-sourced data. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. Thus, the viral information that comes from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms. Participatory data — information that individuals today can produce on their own — enabled by the ubiquity of digital tools, can help fill this gap by providing self-reported data from the community. Internet-based participatory efforts such as Flu Near You have augmented existing ARI surveillance through early and widespread detection of outbreaks and public health trends.

Objective

To evaluate prediction of laboratory diagnosis of acute respiratory infection (ARI) from participatory data using machine learning models

Poliomyelitis a disease targeted for eradication since 1988 still pose public health challenge. The Eastern Mediterranean and African Regions out of the six World Health Organization (WHO) Regions are yet to be certified polio free. The certification of the WHO Africa region is largely dependent on Nigeria, while the WHO Eastern Mediterranean is dependent on Pakistan and Afghanistan. Surveillance for acute flaccid paralysis (AFP) is one of the critical elements of the polio eradication initiative. It provides the needed information to alert health managers and clinician to timely initiate actions to interrupt transmission of the polio disease and evidence for the absence of the wild polio virus. One of the core assignments of the certification committee in all regions is to review documentation to verify the absence of wild poliovirus. Good and complete documentation is the proxy indication of the quality of the system while poor documentation translates to possibilities of missing wild poliovirus in the past. We evaluated the performance of the AFP surveillance system in Bauchi, which is among the 11 high risks states for wild polio virus in Nigeria to identify and address gaps in the surveillance system.

Objective

To identify and address gaps in acute flaccid surveillance for polio eradication in Buchi state

EpiCore draws on the knowledge of a global community of human, animal, and environmental health professionals to verify information on disease outbreaks in their geographic regions. By using innovative surveillance techniques and crowdsourcing these experts, EpiCore enables faster global outbreak detection, verification, and reporting

The NBIC integrates, analyzes, and distributes key information about health and disease events to help ensure the nation’s responses are well-informed, save lives, and minimize economic impact. NBIC serves as a bridge between Federal, State, Local, Territorial, and Tribal entities to conduct biosurveillance across human, animal, plant, and environmental domains. The integration of information enables early warning and shared situational awareness of biological events to inform critical decisions directing response and recovery efforts.

To meet its mission objectives, NBIC utilizes a variety of data sets, including open source information, to provide comprehensive coverage of biological events occurring across the globe. NBIC Biofeeds is a digital tool designed to improve the efficiency of reviewing and analyzing large volumes of open source reporting by biosurveillance analysts on a daily basis; moreover, the system provides a mechanism to disseminate tailored feeds allowing NBIC to better meet the specific information needs of individual, interagency partners. The tool is currently under development by the Department of Energy (DOE), Pacific Northwest National Laboratory (PNNL) and it is in a testing and evaluation phase supported by NBIC biosurveillance subject matter experts. Integration with the Defense Threat Reduction Agency (DTRA), Biosurveillance Ecosystem (BSVE) is also underway. NBIC Biofeeds Version 1 is expected to be fully operational in Fiscal Year 2017. 

Objective

The National Biosurveillance Integration Center (NBIC) is developing a scalable, flexible open source data collection, analysis, and dissemination tool to support biosurveillance operations by the U.S. Department of Homeland Security (DHS) and its federal interagency partners. 

NBIC collects, analyzes, and shares key biosurveillance information to support the nation’s response to biological events of concern. Integration of this information enables early warning and shared situational awareness to inform critical decision making, and direct response and recovery efforts.

DTRA J9 CB leads DoD S&T to anticipate, defend, and safeguard against chemical and biological threats for the warfighter and the nation.

These agencies have partnered to meet the evolving needs of the biosurveillance community and address gaps in technology and data sharing capabilities. High-profile events such as the 2009 H1N1 pandemic, the West African Ebola outbreak, and the recent emergence of Zika virus disease have underscored the need for integration of disparate biosurveillance systems to provide a more functional infrastructure. This allows analysts and others in the community to collect, analyze, and share relevant data across organizations securely and efficiently. Leveraging existing biosurveillance efforts provides the federal public health community, and its partners, with a comprehensive interagency platform that enables engagement and data sharing. 

Objective

The National Biosurveillance Integration Center (NBIC) and the Defense Threat Reduction Agency’s Chemical and Biological Technologies Department (DTRA J9 CB) have partnered to co- develop the Biosurveillance Ecosystem (BSVE), an emerging capability that aims to provide a virtual, customizable analyst workbench that integrates health and non-health data. This partnership promotes engagement between diverse health surveillance entities to increase awareness and improve decision-making capabilities. 

A variety of government reports have cited challenges in coordinating national biosurveillance efforts at strategic and tactical levels. The General Accountability Office (GAO), an independent nonpartisan agency that investigates how the federal government funding and performs analysis at the request of congressional committees or by public mandate, has published 64 reports on biosurveillance since 2005. The aim of this project is to better characterize these issues by collecting and analyzing a sample of publicly documented biosurveillance systems, and making our data and results available for the public health community to review and evaluate. This study openly publishes the data files of information collected (i.e. CSV, XLS), the Python NLP scripts, and a freely available web-based application developed in R Shiny that filters against the 227 biosurveillance systems and activities to promote a more transparent understanding of how public health practitioners conduct surveillance activities.

Objective

The objective of this project is to advance the science of biosurveillance by providing a user curated cataloging system, to be used across health department and other users, that advances daily surveillance operations by better characterizing three key issues in available surveillance systems: duplication in biosurveillance activities; differing perspectives and analyses of the same data; and inadequate information sharing.

Healthcare data, including emergency department (ED) and outpatient health visit data, are potentially useful to the public health community for multiple purposes, including programmatic and surveillance activities. These data are collected through several mechanisms, including administrative data sources [e.g., MarketScan claims data1; American Hospital Association (AHA) data2] andpublic health surveillance programs [e.g., the National Syndromic Surveillance Program (NSSP)3]. Administrative data typically become available months to years after healthcare encounters; however, data collected through NSSP provide near real time information not otherwise available to public health. To date, 46 state and 16 local health departments participate in NSSP, and the estimated nationalp ercentage of ED visits covered by the NSSP BioSense platform is 54%. NSSP’s new data visualization tool, ESSENCE, also includes additional types of healthcare visit (e.g., urgent care) data. Although NSSP is designed to support situational awareness and emergency response, potential expanded use of data collected through NSSP (i.e., by additional public health programs) would promote the utility, value, and long-term sustainability of NSSP and enhance surveillance at the local, state, regional, and national levels. On the other hand, studies using administrative data may help public health programs better understand how NSSP data could enhance their surveillance activities. Such studies could also inform the collection and utilizationof data reported to NSSP.

Objective

This roundtable will address how multiple data sources, including administrative and syndromic surveillance data, can enhance public health surveillance activities at the local, state, regional, and national levels. Provisional findings from three studies will be presented to promote discussion about the complementary uses, strengths and limitations, and value of these data sources to address public health priorities and surveillance strategies.