The ISDS Swap Meet is an exciting opportunity for ISDS pre-conference and conference attendees to walk around to tables and informally discuss particular topics or systems with system developers, system users, and experts.
Veterans Health Administration Selects PraedicoTM Biosurveillance
Payam Etminani, Bitscopic Inc.
PraedicoTM Biosurveillance leverages the leading technology advancements in software architecture, Big Data and machine learning to create a system designed to gather and process huge amounts of data. The same technology advancements are widely used in the private sector by companies like Google and Facebook whose whole business is based on gathering and analyzing hundreds of terabytes of data every day. With sophisticated machine learning algorithms, they are able to better predict the needs of a user and present relevant ads. Praedico Biosurveillance includes the best parts of these new technologies and applies them to the biosurveillance use case to create an application that is easy to use, performs most queries in seconds and enables analysis across single or multiple sets of data from any source.PraedicoTM Biosurveillance was designed to accelerate the speed at which data can be analyzed and used for decision-making. Its ability to perform analysis on various sources of data creates a holistic view of a health related event or problem. With sophisticated mapping and graphing tools, the analysis can be changed on the fly or include other ad hoc data for further clarification or insights. With Praedico Biosurveillance, the monitoring of health-related events will be more real-time, more accurate and easier to share with key decision makers.Veterans Health Administration selected PraedicoTM Biosurveillance to enhance its biosurveillance capabilities. Using Praedico Biosurveillance, the Veterans Health Administration (VHA) will be at the forefront of biosurveillance capabilities and provide improved healthcare outcomes for veterans.
PraedicoTM Biosurveillance application will be available for a live demo during the conference.
A Digital Platform for Foodborne Illness and Outbreak Surveillance
Jared Hawkins, Boston Children’s Hospital
We have developed a platform to enable local and national surveillance of foodborne illness reported on social media and restaurant review sites for supplementing traditional foodborne disease surveillance programs. The platform leverages machine learning and natural language processing to detect potential cases of foodborne illness, and as such is easily scalable. At the Swap Meet Table, we will: 1) demo the dashboard, 2) discuss the pros/cons of different digital data streams, and 3) discuss our collaboration with local public health departments to develop the dashboard. The primary learning objective is for attendees to become familiar with different digital data streams and understand how they can be leveraged for real-time surveillance.
National Syndromic Surveillance Program’s BioSense Platform Implementation Plan
Michael Coletta, Centers for Disease Control and Prevention (CDC)
Join us to ask questions and discuss the changes occurring in the National Syndromic Surveillance Program’s BioSense Platform implementation of ESSENCE, SAS, and R-Studio Professional.
The Biosurveillance Ecosystem (BSVE)
Chistopher Kiley, Defense Threat Reduction Agency (DTRA)
The BSVE is a virtual, customizable, collaborative system that leverages existing commercial and government technologies. The BSVE ingests a wide variety of data sources: open source data; social media, point-of-need diagnostic data; and DoD, Interagency, national and international surveillance system data. Analytic applications “apps” within the BSVE (and user-designed apps) use the aggregated data streams to provide near-real-time modeling, analyses, and visualized results. The BSVE supports the biosurveillance analysts’ and decision-makers’ needs by providing automated, intelligently suggested data, tools, and analyses. The BSVE also provides a user-friendly interface with modern collaboration and reporting features.Ultimately, the BSVE aims to provide a sustainable, user-friendly system that meets the needs of global biosurveillance stakeholders, analysts, and decision makers alike.The BSVE is a cloud-based system on the Amazon (AWS) cloud allowing spontaneous scaling for data storage and computational needs. The BSVE is developed using open source software and systems allowing easier integration, increased transparency for broader user base and customizability, and reduced costs due to licensing. The BSVE ingests and utilizes large data streams such as open source social media feeds, RSS feeds, disease ontologies, de-identified diagnostic results, historic outbreak data, zoonotic data, and non-health data as well as machine learning and natural language processing algorithms to intelligently identify aberrations in disease signals.The BSVE is accessed through a web based interface called the “BSVE Workbench”. It is designed as a platform that contains built-in analytic apps, provides a collaboration and digital workspace for disease monitoring and developing situational reports, and allows developers to design and develop their own apps for BSVE community for free use within an “app store”.The analytic apps developed to date include a variety of applications to perform signal detection, “intelligent” searching, epidemiological curve modeling and analysis, heat mapping, social media data visualization, among others. The broad app approach allows the workbench to be customizable for operational or strategic level analysts as well as decision makers. New applications will be developed and made available to the BSVE community through a Software Development Kit (SDK) and the app store. Additionally, collaboration abilities such as chat, “circles of trust”, in-system report sharing, image sharing, dataset and analysis sharing, and export capabilities from app analyses provide for user-friendly data sharing between experts, analysts, and decision makers.The current prototype includes automated surveillance on global data feeds for over 400 human infectious diseases, novel Twitter analytics, cued federated searching of data ingested into the ecosystem and web searching. Baseline visualizations are provided in all analytics that show potential signal detection. The user can create virtual file folders called “Dossiers” into which results and evidence can be placed and which automatically tracks the origin (provenance) of the information. The Dossier can be stored, updated, shared with other trusted users and used at any time to prepare a report. Information in the Dossier including all accumulated graphics can be clicked for automatic inclusion into a report.
The collaborative, open source, user-friendly, and economically sustainable approach of the BSVE will provide for a unique system that can facilitate rapid and better situational awareness across a wide variety of users, disciplines, and geographic locations. The focus on user-feedback loops and collaboration aims to augment other systems’ use of structured data with analysis of unstructured and aggregated data.
Wayne Loschen, Johns Hopkins University Applied Physics Laboratory (JHU-APL)
Join us to learn more about ESSENCE. There will be a live demonstration site available for you to test drive. We can also discuss any questions you may have related to ESSENCE or what new data sources, features, and visualizations are available.
Emergency Medical Services (EMS) Data – FirstWatch
Todd Stout, FirstWatch Solutions, Inc.
Visitors will learn about the value of EMS data in syndromic, special event, disaster and population surveillance, with case studies, publications and ongoing and published research results. Learn about opportunities to use EMS data for research, and ask questions about EMS data, either in general, or regarding your specific community.
Open Source HL7 Quality Assurance Tool for Syndromic Surveillance
Noam Arzt, HLN Consulting, LLC
Building upon existing Open Source software, we will demonstrate a self-contained, end-to-end prototype of the quality assurance system for syndromic surveillance HL7 messages. This tool can be integrated into the workflow of an agency using Biosense or its own message validation/quality assurance process. The tool supplements the message validation process by providing a way for agency staff (and potentially staff from submitting organizations) to examine messages that have been submitted and review errors and warnings that were indicated by the validation process.
Tools and Apps for Infectious Disease Surveillance
Alina Deshpande, Los Alamos National Laboratory (LANL)
Learning Objectives – To gather information about and learn how to use resources developed by Los Alamos National Laboratory (LANL) for global infectious disease surveillance.Situational awareness is important for both early warning and early detection of a disease outbreak, and analytics and tools that furnish information on how an infectious outbreak would either emerge or unfold provide enhanced situational awareness for decision makers/analysts/public health officials, and support planning for prevention or mitigation. In this demonstration, we will show a suite of tools developed at LANL that provide actionable information and knowledge for enhanced situational awareness during an unfolding event; The surveillance window app (SWAP), the biosurveillance resource directory (BRD), and the biosurveillance analytics resource directory (BARD).SWAP (swap.lanl.gov) – Data obtained in isolation has little value to a decision maker. A local public health official who receives a report about a few cases of gastrointestinal illness in his/her county may be able to plan a more effective infection control strategy if they had knowledge about what the typical disease outbreak curve looks like, the duration of time it takes to spread through a population, how many cases are typical for that region of the world – is it too late to control the infection or do they have time to act? The SWAP is an app to provide a context for a rapidly unfolding event through graphical visualization. It provides a frame of reference for incoming data for a particular location and time during a disease outbreak, to help the user understand and qualify the situation to facilitate actions to prevent, stop or mitigate the event. The SWAP is based on detailed analyses of historical infectious disease specific outbreaks around the globe.BRD (brd.lanl.gov) – is a searchable database, a “one stop shop” for disease surveillance resources and can be used for getting more information received about unfolding disease outbreaks. It contains information on over 500 resources for infectious disease surveillance, representing U.S. (national, state and local) and international government agencies, non-profits, commercial and industry businesses, and academic institutions. A robust and comprehensive framework that describes the fundamental elements of infectious disease surveillance underlies this tool.BARD (bard.lanl.gov/brd) – is a searchable database that catalogs and classifies epidemiological model information in an operational context to allow facile selection of appropriate models for use in disease prediction, monitoring or forecasting. A systematic model characterization framework has been developed to allow consistent epidemiological model description and “apples to apples” comparison for multiple models available for the same disease.
The SWAP, BRD and BARD are available to the US and Global disease surveillance community through LANL’s BSV gateway (bsv.lanl.gov) as well as through independent links. There are no special access requirements.
The Scalable Data Integration for Disease Surveillance System for Integrating Global Health Surveillance Data
Kate Zinszer, McGill University
The Scalable Data Integration for Disease Surveillance (SDIDS) is a software application designed to enable the integration and analysis of data across multiple scales to support global health decision-making. SDIDS is a web-based, ontology-driven software platform that automates the integration of heterogeneous data from multiple sources, and supports visualization, analysis, and sharing of these data. The learning objectives include an introduction to the SDIDS system through three SDIDS clients and to explain the concept of ontology, on which SDIDS is based.
Project SHINE Fellowships
Jessica Pittman, CSTE
The Centers for Disease Control and Prevention (CDC), in collaboration with the Council of State and Territorial Epidemiologists (CSTE) and the National Association of County and City Health Officials (NACCHO) lead Project SHINE to promote population health through interprofessional education, community engagement and health systems integration.
SHINE will illuminate pathways for professionals, organizations, and communities to achieve a collective, transformative, and sustainable impact on population health. This will be achieved through a set of fellowship programs, including but not limited to the Applied Public Health Informatics Fellowship (APHIF) and the Informatics Training in Place Program (I-TIPP).
ISDS Research Committee
Judy Akkina, USDA, APHIS, VS
The ISDS Research Committee promotes research in advanced disease surveillance through multiple activities, such as topical webinars, literature reviews, yearly awards for outstanding research article in biosurveillance and outstanding student abstract, and special interest groups for R statistical software users and Technical Conventions.
ISDS R Group for Disease Surveillance
Howard Burkom, Johns Hopkins University Applied Physics Laboratory (JHU-APL)Objectives of this table are: 1. Attract new novice and experience R users to the group. 2. Get input on past group activities and ideas for future events.
3. Share code, datasets, and approaches acquired thus far
The Technical Conventions Committee and Advancing Analytic Solutions for Biosurveillance
Ian Painter, University of WashingtonThe technical conventions committee works to facilitate development of solutions to unmet analytic needs in public health surveillance. The ISDS has received funding from the Defense Threat Reduction Agency (DTRA) to pursue development of use cases by holding consultancies with public health practitioners and technical solution developers to advance the development of solutions to particular problems.
At this swap meet table we want to hear from public health practitioners about what kinds of surveillance problems public health practitioners are dealing with, and discuss possible models for pursuing development of these problems.
SAGES Suite for Global Electronic BioSurveillance
Shraddha Patel, Johns Hopkins University Applied Physics Laboratory (JHU-APL)
Swap meet participants will learn about the SAGES suite of tools which are intended to bring an open source electronic disease surveillance capacity to resource limited settings. The tools include customizable dashboards, analysis workbench(es), visualizations, mobile and web data entry, and anomaly detection.
Social Medical Analysis and Research Testbed (SMART) Demo
Michael Peddecord, San Diego State University Graduate School of Public Health
To better track, visualize, subset and evaluate events on Twitter, we are continually improving the online Social Media Analysis and Research Testbed (SMART). The dashboard currently tracks: influenza, pertussis, HIV, Ebola and vaccine exemptions. Recently added is analysis and display of 34 syndromic keywords in 8 categories. Components of the dashboard include: expandable timeline displaying raw and filtered tweets; top rated re-tweets; top rated websites mentioned in tweets; and mapping of tweet rates from combinations of 30+ cities in N. America. Displays include: top hash tags, a word cloud with word count bar graph, display of top media during selected periods of time. For in-depth review, tweet subsets can be downloaded and displayed from any dashboard component. Website: http://vision.sdsu.edu/hdma/smart/
USDA APHIS Veterinary Services
Judy Akkina, USDA
The overall mission of USDA APHIS VS is to protect and improve the health, quality, and marketability of our nation’s animals, animal products and veterinary biologics by preventing, controlling and/or eliminating animal diseases, and monitoring and promoting animal health and productivity. In order to accomplish this mission, VS is involved in many disease surveillance activities.