Biosurveillance Ecosystem Research Articles

Data Science, Analytics and Collaboration for a Biosurveillance Ecosystem

Data Science, Analytics and Collaboration for a Biosurveillance Ecosystem

Epi Archive: Automated Synthesis of Global Notifiable Disease Data

ObjectiveAutomatically collect and synthesize global notifiable disease data and make it available to humans and computers. Provide the data on the web and within the Biosurveillance Ecosystem (BSVE) as a novel data stream. These data have many applications including improving the prediction and early warning of disease events.IntroductionGovernment reporting of notifiable disease data is common and widespread, though most countries do not report in a machine-readable format. This is despite the WHO International Health Regulations stating that “[e]ach State Party shall notify WHO, by the most efficient means of communication available.” 1Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health. While most nations likely store incident data in a machine-readable format, governments can be hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational2.A survey conducted by LANL of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. The majority (>70%) produce reports as PDF files on a regular basis. The bulk of the PDF reports present data in a structured tabular format, while some report in natural language or graphical charts.The structure and format of PDF reports change often; this adds to the complexity of identifying and parsing the desired data. Not all websites publish in English, and it is common to find typos and clerical errors.LANL has developed a tool, Epi Archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible.MethodsA survey of the national notifiable disease reporting systems is periodically conducted notating how the data are reported and in what formats. We determined the minimal metadata that is required to contextualize incident counts properly, as well as optional metadata that is commonly found.The development of software to regularly ingest notifiable disease data and make it available involves three to four main steps: scraping, detecting, parsing and persisting.Scraping: we examine website design and determine reporting mechanisms for each country/website, as well as what varies across the reporting mechanisms. We then design and write code to automate the downloading of data for each country. We store all artifacts presented as files (PDF, XLSX, etc.) in their original form, along with appropriate metadata for parsing and data provenance.Detecting: This step is required when parsing structured non-machine-readable data, such as tabular data in PDF files. We combine the Nurminen methodology of PDF table detection with in-house heuristics to find the desired data within PDF reports3.Parsing: We determine what to extract from each dataset and parse these data into uniform data structures, correctly accommodating the variations in metadata (e.g., time interval definitions) and the various human languages.Persisting: We store the data in the Epi Archive database and make it available on the internet and through the BSVE. The data is persisted into a structured and normalized SQL database.ResultsEpi Archive currently contains national and/or subnational notifiable disease data from thirty-nine nations. When a user accesses the Epi Archive site, they are able to peruse, chart and download data by country, subregion, disease and time interval. Access to a cached version of the original artifacts (e.g. PDF files), a link to the source and additional metadata is also available through the user interface. Finally, to ensure machine-readability, the data from Epi Archive can be reached through a REST API. http://epiarchive.bsvgateway.org/ConclusionsLANL, as part of a currently funded DTRA effort, is automatically and continually collecting global notifiable disease data. While thirty-nine nations are in production, more are being brought online in the near future. These data are already being utilized and have many applications, including improving the prediction and early warning of disease events.

Wearable Sensor Application for Integrated Early Warning and Health Surveillance

Objective: The Wearable Sensor Application developed by Pacific Northwest National Laboratory (PNNL) provides an early warning system for stressors to individual and group health using physiologic and environmental indicators. The application integrates health monitoring parameters from wearable sensors, e.g., temperature and heart rate, with relevant environmental parameters, e.g., weather and landscape data, and calculates the corresponding physiological strain index. The information is presented to the analyst in a group and individual view with real-time alerting of abnormal health parameters. This application is the first of its kind being developed for integration into the Defense Threat Reduction Agency's Biosurveillance Ecosystem (BSVE).Introduction: Wearable devices are a low cost, minimally invasive way to monitor health. Sensor data provides real-time physiological indictors of an individual’s health status without the requirement of health care professionals or facilities. Information gleamed from wearable sensors can be used to better understand physiological stressors and prodromal symptoms. In addition, this data can be used to monitor individuals that are in high risk of health-related problems.However, raw data from wearable sensors can be overwhelming to process and laborious to monitor for an individual and, even more so, for a group of individuals. Often specific combination of ranges of sensor readings are indicative of changes to health status and need to be evaluated together or used to calculate specific signal parameters. In addition, the environment surrounding the individual needs to be considered when interpreting the data. To address these issues, PNNL has developed an application that collects, analyzes, and integrates wearable sensor data with geographic landscape and weather information to provide a real-time early alert and situational awareness tool for monitoring the health of groups and individuals.Methods: The prototype application described here was a product of PNNL’s BSVE Application Development Competition. The final product that will be deployed in the BSVE is currently under development by PNNL and will vary slightly in the exact design and architecture described.Data. Wearable sensor data was collected from the Rim2Rim (R2R) Watch Study of individuals hiking the Grand Canyon in Arizona [1]. Weather information was obtained from nearby weather stations and mapping features were derived from Google Maps.Calculations. A physiological Strain Index (PSI) was calculated using core temperature estimates derived through a Kalman Filter approach and heart rate [2,3].Application. The prototype backend application development was based in Python with a MongoDB. The front-end development was built using a scalable architecture and modular approach with components in React and D3.Results: A prototype application was developed this past summer through the PNNL BSVE App Competition (Fig 1). The application was aimed at visualizing wearable sensor data from the Grand Canyon R2R hike dataset. Simulated real-time analysis was used to calculate health status of individuals hiking based on measured physiological parameters and to alert to individuals with signs of physiologic health stress. Visualization tools were incorporated to enable sensor data for individuals and the group to be viewed simultaneously along with pertinent weather, geographic, and elevation data.Many features described in the prototype application will be incorporated into the final BSVE application. The key changes will be 1) the ability to select given time periods for viewing historical data as well as the real-time data collection, 2) environmental data and map view will come from BSVE internal data sources, and 3) the alerts will provide more information and have their own page for reviewing.Conclusions: The Wearable Sensor Application developed by PNNL for integration into the BSVE provides an early warning system for individual and group health using physiologic and environmental parameters. The application highlights health status from wearable sensors and relevant environmental parameters while monitoring a calculated physiological strain index. With this tool, an analyst can easily monitor the health of individuals and groups with the aid of real-time alerting tool for early detection of abnormal health parameters.

Epi Archive: Automated Synthesis of Global Notifiable Disease Data

ObjectiveLANL has built software that automatically collects global notifiable disease data, synthesizes the data, and makes it available to humans and computers within the Biosurveillance Ecosystem (BSVE) as a novel data stream. These data have many applications including improving the prediction and early warning of disease events.IntroductionMost countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health.While most nations likely store incident data in a machine-readable format, governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues1.A survey conducted by LANL of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. The majority (>70%) produce reports as PDF files on a regular basis. The bulk of the PDF reports present data in a structured tabular format, while some report in natural language.The structure and format of PDF reports change often; this adds to the complexity of identifying and parsing the desired data. Not all websites publish in English, and it is common to find typos and clerical errors.LANL has developed a tool, Epi Archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible.MethodsWe conducted a survey of the national notifiable disease reporting systems notating how the data are reported and in what formats. We determined the minimal metadata that is required to contextualize incident counts properly, as well as optional metadata that is commonly found.The development of software to regularly ingest notifiable disease data and make it available involves three or four main steps: scraping, detecting, parsing and persisting.Scraping: we examine website design and determine reporting mechanisms for each country/website, as well as what varies across the reporting mechanisms. We then designed and wrote code to automate the downloading of the data for each country. We store all artifacts presented as files (PDF, XLSX, etc.) in their original form, along with appropriate metadata for parsing and data provenance.Detecting: This step is required when parsing structured non-machine-readable data such as tabular data in PDF files. We combined the Nurminen methodology of PDF table detection with in-house heuristics to find the desired data within PDF reports2.Parsing: We determined what to extract from each dataset and parsed these data into uniform data structures, correctly accommodating the variations in metadata (e.g., time interval definitions) and the various human languages.Persisting: We store the data in the Epi Archive database and make it available on the internet and through the BSVE. The data is persisted into a structured and normalized SQL database.ResultsThe Epi Archive tool currently contains national and/or subnational notifiable disease data from twenty nations. When a user accesses the Epi Archive site, they are prompted with four fields: country, subregion, disease of interest, and date duration. Upon form submission, a time series is generated from the users’ specifications. The generated graph can then be downloaded into a CSV file if a user is interested in performing personal analysis. Additionally, the data from Epi Archive can be reached through a REST API (Representational State Transfer Application Programming Interface).ConclusionsLANL, as part of a currently funded DTRA effort, is automatically and continually collecting global notifiable disease data. While 20 nations are in production, more are being brought online in the near future. These data are already being utilized and will have many applications including improving the prediction and early warning of disease events.

NBIC Biofeeds: Deploying a New, Digital Tool for Open Source Biosurveillance across Federal Agencies

ObjectiveThe National Biosurveillance Integration Center (NBIC) is deploying 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.IntroductionNBIC 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. 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 analyzing large volumes of open source reporting and increase the number of relevant insights gleaned from this dataset. Moreover, the tool provides a mechanism to disseminate tailored, electronic message notifications in near-real time so that NBIC can share specific information of interest to its interagency partners in a timely manner.MethodsNBIC intends to implement operational use of the capability in FY 2018. The core components of the system are data collection, curation, and dissemination of information deemed important by NBIC subject matter experts. NBIC Biofeeds has captured information from more than 70,000 unique sources published from around the globe and presents, on average, 9,000 new biosurveillance-relevant articles to users each day. NBIC leverages a variety of data feeds, including third party aggregators like Google and subscription-based feeds such as HealthMap, as well as Really Simple Syndication (RSS) feeds and web-scraping of highly relevant sources.The NBIC biosurveillance taxonomy imbedded in the tool consists of more than 600 metadata targets that cover key information for understanding the significance of an active biological event, including etiologic agents, impact to humans and animals (e.g., infection severity, healthcare workers involved, type of host), social disruption, infrastructure strain, countermeasures engaged, and ‘red flag’ characteristics (e.g., pathogen appearance in a new geographic area, unusual clinical signs). This taxonomy serves as a foundation for data curation and can be tailored by NBIC partners to more directly meet their own mission objectives.At this time, metadata is predominately captured by NBIC analysts, who manually tag information, which triggers the population of three automatically-disseminated products from the tool: 1) the NBIC Daily Biosurveillance Review, 2) immediate and daily summary email notifications, and 3) custom-designed RSS feeds. These products are meant for individual recipients in the federal interagency and for consumption by other biosurveillance information technology systems, such as the Department of Defense, Defense Threat Reduction Agency (DTRA) Biosurveillance Ecosystem (BSVE). NBIC is working in partnership with DTRA to integrate NBIC Biofeeds as an application directly into the BSVE and further develop the BSVE as an all-in-one platform for biosurveillance data analytics.To improve the efficiency and effectiveness of gaining insights using NBIC Biofeeds, developers of the tool at the Pacific Northwest National Laboratory (PNNL) are researching and testing a variety of advanced analytics techniques focused on: 1) article relevancy ratings to improve the review of queried data, 2) significance ratings to elucidate the perceived severity of an event based on reported characteristics, 3) full-text article retrieval and storage for improved machine-tagging, and 4) anomaly detection for emerging threats. Testing and implementation of new analytic capabilities in NBIC Biofeeds is planned for this fiscal year.ResultsNBIC Biofeeds was developed to serve as a sophisticated and powerful open source biosurveillance technology of value to the federal government by providing information to stakeholders conducting open source biosurveillance as well as those consuming biosurveillance information. In FY 2018, NBIC Biofeeds will begin operational use by NBIC and an initial set of users in various federal agencies. User accounts for testing purposes will be available to other federal partners, and a broad scope of federal stakeholders can receive products directly from NBIC Biofeeds based on their interests.ConclusionsNBIC Biofeeds is expected to enable more rapid recognition and enhanced analysis of emerging biological events by NBIC analysts. NBIC anticipates other federal agencies with biosurveillance missions will find this technology of value and intends to offer use of the platform to those federal partners that can benefit from access to the tool and information generated from NBIC Biofeeds.

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

ObjectiveEpi Evident is a web based application built to empower public health analysts by providing a platform that improves monitoring, comparing, and forecasting case counts and period prevalence of notifiable diseases for any scale jurisdiction at regional, country, or global-level. This proof of concept application development addresses improving visualization, access, situational awareness, and prediction of disease behavior.IntroductionThe 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. Developed at the Pacific Northwest National Laboratory (PNNL), Epi Evident utilizes time-series disease case count data from the Biosurveillance Ecosystem (BSVE) application Epi Archive (1). This diverse data source is filtered through the flexible Epi Evident workflow for forecast model building designed to integrate any entering combination of country and disease. The application aims to quickly inform analysts of anomalies in disease & location specific behavior and aid in evidence based decision making to help control or prevent disease outbreaks.MethodsA workflow was constructed to define the best disease forecast model for each location based on an adjustable method approach. The differences in disease behavior across countries was achieved through a React/Python application with a user-friendly output for monitoring and comparing different combinations.The forecast model building workflow consisted of three major steps to determine the best fit model for a given disease-country pair: data type, model type, and model comparison & selection. Testing various disease-country combinations allowed for direct evaluation of the workflow efficiency, flexibility, and criteria for determining the best fit model. Data type was characterized as either seasonal, cyclic, or sporadic. Depending on data type, a specific time series forecasting model was applied. In general, seasonal or cyclic data required either an Auto-Regression Integrated Moving Average (ARIMA) model or a Seasonal Auto-Regression Integrated Moving Average (SARIMA) model while sporadic datasets employed a Poisson model. Several model candidates for a single country and disease combination were then compared to determine which was the best fit model. ARIMA and SARIMA model selection criteria included their respective order significance, residual diagnostics, and lowest possible combination of Akaike Information Criterion and Root Mean Square Error (RMSE) values. Poisson model selection criteria involved Poisson or negative binomial distribution and event probability, lag dependency of immediate past events or seasonality, and lowest possible RMSE. To enhance the user’s monitoring and comparisons across multiple countries and diseases, each forecasted case counts supplied a corresponding period prevalence. This period prevalence was calculated by dividing the case counts by the population in the selected country and timeframe. Population records were obtained through the public World Health Organization database (2).ResultsA variety of visualization tools on Epi Evident allows convenient interpretation on behaviors of diseases spanning multiple countries simultaneously (Figure 1). Countries, diseases, and timeframe are selected and displayed within a matrix alongside with their corresponding forecasts for case counts and period prevalence. By providing this full representation, users can easily interpret and anticipate disease behavior while monitoring, comparing, and forecasting case counts and period prevalence across multiple countries. For future work, the Epi Evident workflow can be scaled to accommodate any disease-country combination with automated model selection to allow easier and more efficient biosurveillance.ConclusionsEpi Evident empowers analysts to visualize, monitor, compare, and forecast disease case counts and period prevalence across countries. Epi Evident exemplifies how filtering diverse data through a flexible workflow can be scalable to output distinctive models for any given country and disease combination. Thus, providing accurate forecasting and enhanced situational awareness throughout the globe. Implementing this application’s methodology helps enhance and expand biosurveillance efficacy for multiple diseases across multiple countries simultaneously.

Soda Pop: A Time-Series Clustering, Alarming and Disease Forecasting Application

ObjectiveTo introduce Soda Pop, an R/Shiny application designed to be adisease agnostic time-series clustering, alarming, and forecastingtool to assist in disease surveillance “triage, analysis and reporting”workflows within the Biosurveillance Ecosystem (BSVE) [1]. In thisposter, we highlight the new capabilities that are brought to the BSVEby Soda Pop with an emphasis on the impact of metholodogicaldecisions.IntroductionThe Biosurveillance Ecosystem (BSVE) is a biological andchemical threat surveillance system sponsored by the Defense ThreatReduction Agency (DTRA). BSVE is intended to be user-friendly,multi-agency, cooperative, modular and threat agnostic platformfor biosurveillance [2]. In BSVE, a web-based workbench presentsthe analyst with applications (apps) developed by various DTRAfundedresearchers, which are deployed on-demand in the cloud(e.g., Amazon Web Services). These apps aim to address emergingneeds and refine capabilities to enable early warning of chemical andbiological threats for multiple users across local, state, and federalagencies.Soda Pop is an app developed by Pacific Northwest NationalLaboratory (PNNL) to meet the current needs of the BSVE forearly warning and detection of disease outbreaks. Aimed for use bya diverse set of analysts, the application is agnostic to data sourceand spatial scale enabling it to be generalizable across many diseasesand locations. To achieve this, we placed a particular emphasis onclustering and alerting of disease signals within Soda Pop withoutstrong prior assumptions on the nature of observed diseased counts.MethodsAlthough designed to be agnostic to the data source, Soda Pop wasinitially developed and tested on data summarizing Influenza-LikeIllness in military hospitals from collaboration with the Armed ForcesHealth Surveillance Branch. Currently, the data incorporated alsoincludes the CDC’s National Notifiable Diseases Surveillance System(NNDSS) tables [3] and the WHO’s Influenza A/B Influenza Data(Flunet) [4]. These data sources are now present in BSVE’s Postgresdata storage for direct access.Soda Pop is designed to automate time-series tasks of datasummarization, exploration, clustering, alarming and forecasting.Built as an R/Shiny application, Soda Pop is founded on the powerfulstatistical tool R [5]. Where applicable, Soda Pop facilitates nonparametricseasonal decomposition of time-series; hierarchicalagglomerative clustering across reporting areas and between diseaseswithin reporting areas; and a variety of alarming techniques includingExponential Weighted Moving Average alarms and Early AberrationDetection [6].Soda Pop embeds these techniques within a user-interface designedto enhance an analyst’s understanding of emerging trends in their dataand enables the inclusion of its graphical elements into their dossierfor further tracking and reporting. The ultimate goal of this softwareis to facilitate the discovery of unknown disease signals along withincreasing the speed of detection of unusual patterns within thesesignals.ConclusionsSoda Pop organizes common statistical disease surveillance tasksin a manner integrated with BSVE data source inputs and outputs.The app analyzes time-series disease data and supports a robust set ofclustering and alarming routines that avoid strong assumptions on thenature of observed disease counts. This attribute allows for flexibilityin the data source, spatial scale, and disease types making it useful toa wide range of analystsSoda Pop within the BSVE.KeywordsBSVE; Biosurveillance; R/Shiny; Clustering; AlarmingAcknowledgmentsThis work was supported by the Defense Threat Reduction Agency undercontract CB10082 with Pacific Northwest National Laboratory

Epi Archive: automated data collection of notifiable disease data

ObjectiveLANL has built a software program that automatically collectsglobal notifiable disease data—particularly data stored in files—andmakes it available and shareable within the Biosurveillance Ecosystem(BSVE) as a new data source. This will improve the prediction andearly warning of disease events and other applications.IntroductionMost countries do not report national notifiable disease data in amachine-readable format. Data are often in the form of a file thatcontains text, tables and graphs summarizing weekly or monthlydisease counts. This presents a problem when information is neededfor more data intensive approaches to epidemiology, biosurveillanceand public health as exemplified by the Biosurveillance Ecosystem(BSVE).While most nations do likely store their data in a machine-readableformat, the governments are often hesitant to share data openly fora variety of reasons that include technical, political, economic, andmotivational issues [1]. For example, an attempt by LANL to obtaina weekly version of openly available monthly data, reported by theAustralian government, resulted in an onerous bureaucratic reply. Theobstacles to obtaining data included: paperwork to request data fromeach of the Australian states and territories, a long delay to obtaindata (up to 3 months) and extensive limitations on the data’s use thatprohibit collaboration and sharing. This type of experience whenattempting to contact public health departments or ministries of healthfor data is not uncommon.A survey conducted by LANL of notifiable disease data reportingin 52 countries identified only 10 as being machine-readable and42 being reported in pdf files on a regular basis. Within the 42 nationsthat report in pdf files, 32 report in a structured, tabular format and10 in a non-structured way.As a result, LANL has developed a tool-Epi Archive (formerlyknown as EPIC)-to automatically and continuously collect globalnotifiable disease data and make it readily accesible.MethodsWe conducted a survey of the national notifiable disease reportingsystems notating how the data is reported in two important dimensions:date standards and case definitions.The development of software to regularly ingests notifiabledisease data frand makes this data available involved four main stepsscraping, extracting, parsing and persisting.For scraping: we would examine website designs and determinereporting mechanisms for each country/website as well as what variesacross the reporting mechanisms. We then designed and wrote codeto automate the downloading of report pdf files, for each country.We stored report pdfs along with appropriate metadata for extractingand parsing.For extracting: we developed software that can extract notifiabledisease data presented in tabular form from a pdf file. We combinedthe methodology of figure placement detection with the in-housedeveloped table extraction and annotation heuristics.For parsing: we determined what to extract from each pdf dataset from the survey conducted. We then parsed the extracted datainto uniform data structures correctly accommodating the dimensionssurveyed and the various human languages. This task involvedingesting notifiable disease data in many disparate formats extractedfrom pdf files and coalescing the data into a standardized format.For persisting: We then store the data in the Epi ArchivePostgreSQL database and make it available through the BSVE.ResultsThe EpiArchive tool currently contains subnational notifiabledisease data from 10 nations. When a user accesses the EpiArchivesite, they are prompted with four fields: country, region, disease,and date duration. These fields allow the user to specify the location(down to the state level), the disease of interest, and the durationof interest. Upon form submission, a time series is generated fromthe users’ specifications. The generated time series can then bedownloaded into a csv file if a user is interested in performingpersonal analysis. Additionally, the data from EpiArchive can bereached through an API.ConclusionsLANL as part of a currently funded DTRA effort so that it willautomatically and continuously collect global notifiable diseasedata—particularly data stored in pdf files—and make it available andshareable within the Biosurveillance Ecosystem (BSVE) as a newdata source. This will provide data to analytics and users that willimprove the prediction and early warning of disease events and otherapplications.

NBIC and DTRA, An Interagency Partnership to Integrate Analyst Capabilities

ObjectiveThe National Biosurveillance Integration Center (NBIC) andthe Defense Threat Reduction Agency’s Chemical and BiologicalTechnologies Department (DTRA J9 CB) have partnered to co-develop the Biosurveillance Ecosystem (BSVE), an emergingcapability that aims to provide a virtual, customizable analystworkbench that integrates health and non-health data. This partnershippromotes engagement between diverse health surveillance entities toincrease awareness and improve decision-making capabilities.IntroductionNBIC collects, analyzes, and shares key biosurveillanceinformation to support the nation’s response to biological events ofconcern. Integration of this information enables early warning andshared situational awareness to inform critical decision making, anddirect response and recovery efforts.DTRA J9 CB leads DoD S&T to anticipate, defend, and safeguardagainst chemical and biological threats for the warfighter and thenation.These agencies have partnered to meet the evolving needs of thebiosurveillance community and address gaps in technology and datasharing capabilities. High-profile events such as the 2009 H1N1pandemic, the West African Ebola outbreak, and the recent emergenceof Zika virus disease have underscored the need for integration ofdisparate biosurveillance systems to provide a more functionalinfrastructure. This allows analysts and others in the communityto collect, analyze, and share relevant data across organizationssecurely and efficiently. Leveraging existing biosurveillance effortsprovides the federal public health community, and its partners, witha comprehensive interagency platform that enables engagement anddata sharing.MethodsNBIC and DTRA are leveraging existing biosurveillance projectsto share data feeds, work processes, resources, and lessons learned.A multi-stakeholder Agile process was implemented to representthe interests of NBIC, DTRA, and their respective partners. Systemrequirements generated by both agencies were combined to form asingle backlog of prioritized needs. Functional requirements fromNBIC support the development of the prototype by refining systemcapabilities and providing an operational perspective. DTRA’stechnical expertise and research and development (R&D) portfolioensures robust analytic applications are embedded within a secure,scalable system architecture.Integration of analyst validated data from the NBIC Biofeedssystem serves as a gold-standard to improve analytic developmentin machine learning and natural language processing. Additionally,working groups are formed using NBIC and DTRA extendedpartnerships with academia and private industry to expand R&Dpossibilities. These expansions include leveraging existing ontologyefforts for improved system functionality and integrating social mediaalgorithms for improved topic analysis output.ResultsThe combined efforts of these two agencies to develop theBSVE and improve overall biosurveillance processes across thefederal government has enhanced understanding of the needs ofthe community in a variety of mission spaces. To date, co-creation ofproducts, joint analysis, and sharing of data feeds has become a majorpriority for both partners to advance biosurveillance outcomes. Withinthe larger efforts of system development, possible coordination withother agencies such as the Department of Veterans Affairs (VA) andthe US Geological Survey (USGS) could expand reach of the systemto ensure fulfillment of health surveillance requirements as a whole.ConclusionsThe NBIC and DTRA partnership has demonstrated value inimproving biosurveillance capabilities for each agency and theirpartners. BSVE will provide NBIC analysts with a collaborativetool that can leverage use of applications that visualize near real-time global epidemic and outbreak data from a range of unique andtrusted sources. The continued collaboration means ongoing accessto new data streams and analytic processes for all analysts, as wellas advanced machine learning algorithms that increase capabilitiesfor joint analysis, rapid product creation, and continuous interagencycommunication.

NBIC Biofeeds: A Digital Tool for Open Source Biosurveillance across Federal Agencies

ObjectiveThe National Biosurveillance Integration Center (NBIC) isdeveloping a scalable, flexible open source data collection, analysis,and dissemination tool to support biosurveillance operations bythe U.S. Department of Homeland Security (DHS) and its federalinteragency partners.IntroductionThe NBIC integrates, analyzes, and distributes key informationabout health and disease events to help ensure the nation’s responsesare well-informed, save lives, and minimize economic impact. NBICserves as a bridge between Federal, State, Local, Territorial, andTribal entities to conduct biosurveillance across human, animal, plant,and environmental domains. The integration of information enablesearly warning and shared situational awareness of biological eventsto inform critical decisions directing response and recovery efforts.To meet its mission objectives, NBIC utilizes a variety of datasets, including open source information, to provide comprehensivecoverage of biological events occurring across the globe. NBICBiofeeds is a digital tool designed to improve the efficiency ofreviewing and analyzing large volumes of open source reportingby biosurveillance analysts on a daily basis; moreover, the systemprovides a mechanism to disseminate tailored feeds allowing NBIC tobetter meet the specific information needs of individual, interagencypartners. The tool is currently under development by the Departmentof Energy (DOE), Pacific Northwest National Laboratory (PNNL)and it is in a testing and evaluation phase supported by NBICbiosurveillance subject matter experts. Integration with the DefenseThreat Reduction Agency (DTRA), Biosurveillance Ecosystem(BSVE) is also underway. NBIC Biofeeds Version 1 is expected tobe fully operational in Fiscal Year 2017.MethodsThe PNNL is applying agile methodology to streamline the buildof NBIC Biofeeds to specifications required for operational use byNBIC and its federal interagency partners. Biosurveillance, analytics,and system engineering subject matter experts provide guidance onthe implementation of features in the tool to ensure functionalityaligns with operational workflows and production support. PNNL isleveraging software from a previous government effort to repurposethe technology to meet NBIC needs. NBIC Biofeeds incorporatesthe open source, document-orientated MongoDB database to captureuser- and system-generated metadata on hundreds of thousandsof records, in part, to establish baselines to aid prospective andretrospective analysis on emerging biological events. NBIC Biofeedsintegrates a biosurveillance taxonomy (uniquely developed by NBIC),which includes input from interagency partners to recognize criticalcharacteristics of a biological event. In NBIC Biofeeds Version1, metadata capture of reported events is done manually by NBICanalysts; however, moving forward in Version 2, the tool will befurther automated to flag significant reporting on biological eventswith a human remaining in the loop to confirm the validity of thesystem-generated tags.ResultsTo serve as a one-stop tool for open source biosurveillance,NBIC Biofeeds automatically harvests information from thousandsof websites, utilizing third party aggregators, paid subscriptions todata feeds, and scraping of high priority sources. Users can developdesired queries for automatic updating, leverage a unique reviewand curation mechanism, and further analyze data from topical,geographic, and temporal visualization features in the tool. To meetNBIC’s information sharing needs, the tool allows for design oftailored RSS feeds and electronic message-based delivery of analysison biological events, intended for recipients in the government withunique missions around human, animal, plant, and environmentalhealth.ConclusionsThrough current testing and evaluation – underway bybiosurveillance subject matter experts – NBIC Biofeeds isdemonstrating value in supporting open source biosurveillanceby the Center for more rapid recognition and sharing of key eventcharacteristics. Centralizing access and analysis of this datasetinto a single system is increasing the efficiency of daily, globalbiosurveillance, while enhancing the value of information identifiedthrough use of the querying, curation, and production support featuresin the tool.

Novel Analysis and Visualization of Chemical Events for Public Health Surveillance

ObjectivePacific Northwest National Laboratory hosted an intern-basedweb application development contest in the summer of 2016 centeredaround developing novel chemical surveillance applications to aid inhealth situational awareness. Making up the three teams were threegraduate students (n=9) from various US schools majoring in non-public health domains, such as computer sicence and user design. Theinterns suc- cessfully developed three applications that demonstrateda value-add to chemical surveillance—ChemAnalyzer (textanalytics), RetroSpect (retrospective analysis of chemical events),and ToxicBusters (geo-based trend analytics). These applicationswill be the basis for the first chemical surveillance application to beincorporated into the DTRA Biosurveillance Ecosystem (BSVE).IntroductionPacific Northwest National Laboratory (PNNL), on behalf theDefense Threat Reduction Agency (DTRA; project number CB10190),hosts an annual intern- based web app development contest. Previouscompetitions have focused on mobile biosurveillance applications.The 2016 competition pivoted away from biosurveillance to focus onaddressing challenges within the field of chemical surveillance andincreasing public health chemical situational awareness. The result ofthe app will be integrated within the DTRA BSVE.MethodsPNNL hosted nine graduate interns for a 10-week period inthe summer of 2016 as participants in a summer web applicationdevelopment contest. Students were drawn from such fields assoftware engineering and user experience and design and placedinto three teams of three students. The challenge presented to theinterns was to design and develop a fully-functional web applicationthat would address a critical need within the chemical surveillancecommunity. The interns developed their own ideas (vetted by PNNLand DTRA), discovered and inte- grated their own data sources,and produced their own visualizations and an- alytics, independentof any assistence outside of that provided in an advisory capacity.The competition end with a judging event with a panel of subjectmatter experts and cash awards were distributed to the teams.ResultsEach team produced a unique application. Although there wasmild overlap between some of the ideas, the applications weredeveloped independently and each reflected the unique contributionsof the teams. ChemAnalyzer is a text-analytics platform designedto facilitate more data- driven decision, given a corpus of text dataabout a chemical event. Their plat- form provided the ability toautomatically identify and highlight key words in documents relatedto chemical events. The keywords are drawn from an on- tologyinstalled with the system, as well as any user-identified keywords.The ChemAnalyzer team finished in third place. The RetroSpect teamdeveloped a visual analytic tool for performing retrospec- tive analysisand monitoring of chemical events. Their app provided the ability tosearch and analyze past events, as well as visualization of state andcounty information for the recorded chemical events. The RetroSpectteam finished in second place. The Toxicbusters team—the winnersof the competition—created a geo-based situational awareness toolfor tracking chemical events. Their app featured an updateable mapoverlay, search functionality for finding specific or related events,incident and city/state/national-level statistics and trends, as wellas news and social media integration based on keywords related tochemical surveillance.ConclusionsEach of the apps developed by the teams provides value to ananalyst tasked with monitoring chemical events. The apps integratedunique data sources to provides a full picture of a chemical event, andits effects upon the surrounding population. This integrated analyticsprovides a valuable benefit over existing workflows, where analystsmust monitor news, social, and other information sources manuallyfor real-time information. The apps developed by these interns aredesigned to enable identification and analysis of the incident asquickly as possible, allowing for more timely assessments of theincident and its impacts. The web app development contest provideda unique opportunity for students to learn about the emergingneeds in chemical surveillance as it relates to health sit- uationalawareness. Students were drawn from a variety of fields and weretasked with developing novel web apps addressing some of the mostpressing challenges in the field of chemical surveillance. The ideasgenerated by the students will help form the basis for future chemicalsurveillance application development to be integrated with the DTRABSVE.

Cross-Disciplinary Consultancy to Bridge Public Health Technical Needs and Analytic Developers: Asyndromic Surveillance Use Case.

Introduction: We document a funded effort to bridge the gap between constrained scientific challenges of public health surveillance and methodologies from academia and industry. Component tasks are the collection of epidemiologists’ use case problems, multidisciplinary consultancies to refine them, and dissemination of problem requirements and shareable datasets. We describe an initial use case and consultancy as a concrete example and challenge to developers.Materials and Methods: Supported by the Defense Threat Reduction Agency Biosurveillance Ecosystem project, the International Society for Disease Surveillance formed an advisory group to select tractable use case problems and convene inter-disciplinary consultancies to translate analytic needs into well-defined problems and to promote development of applicable solution methods. The initial consultancy’s focus was a problem originated by the North Carolina Department of Health and its NC DETECT surveillance system: Derive a method for detection of patient record clusters worthy of follow-up based on free-text chief complaints and without syndromic classification.Results: Direct communication between public health problem owners and analytic developers was informative to both groups and constructive for the solution development process. The consultancy achieved refinement of the asyndromic detection challenge and of solution requirements. Participants summarized and evaluated solution approaches and discussed dissemination and collaboration strategies.Practice Implications: A solution meeting the specification of the use case described above could improve human monitoring efficiency with expedited warning of events requiring follow-up, including otherwise overlooked events with no syndromic indicators. This approach can remove obstacles to collaboration with efficient, minimal data-sharing and without costly overhead.

COMBS: A Biosurveillance Ecosystem (BSVE) Prototype

The goal of DTRA's Biosurveillance Ecosystem (BSVE) program is to significantly reduce the time required to identify threats to human health, whether of malicious or natural origin, and respond appropriately. Draper Laboratory is leading a team to develop the Collaborative Overarching Multi-feed Biosurveillance System (COMBS) for BSVE to revolutionize biosurveillance capabilities. USG analysts whose jobs are to reduce risks to national security will benefit from rapid and thorough information access, as will local public health authorities and individual citizens. This poster will provide an overview of the COMBS project and current progress towards meeting the goals of the BSVE.

Simulation-based Testbed for Bio-Surveillance Systems

The U.S. Defense Threat Reduction Agency (DTRA) is funding multiple development efforts directed at enhanced platforms to support bio-surveillance analysts under their Bio-surveillance Ecosystem (BSVE) program. These efforts include well-integrated user interface systems and advanced algorithmic concepts to facilitate analysis of diverse, pertinent data sources including traditional bio-surveillance data sources as well as social media inputs. A central challenge in this development effort is a practical, effective, method to test these prototype systems. This presentation discusses a simulation-based testbed to allow quantitative evaluation of analytical methods through controlled injection of simulated outbreak-related information into test data streams.

Biosurveillance Ecosystem (BSVE) Workflow Analysis

The Defense Threat Reduction Agency Chemical and Biological Technologies Directorate (DTRA CB) is funding the development of the Biosurveillance Ecosystem (BSVE). Biosurveillance operational capability gaps were analyzed through work process discussions with several disease surveillance organizations. Two meta-workflows were evident. In the first type, epidemiologists identify and characterize health-impacting events to enable community-level responses to the event. The second type is more focused on informing leadership and response in the form of policy modification. Analysts described features of a future biosurveillance environment that they wish were available in various categories: data, enhanced search, verification, analytics, collaboration and communication, and archival.