Tools and Apps to Enhance Situational Awareness for Global ... · Awareness for Global Disease...
Transcript of Tools and Apps to Enhance Situational Awareness for Global ... · Awareness for Global Disease...
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Tools and Apps to Enhance Situational Awareness for Global Disease Surveillance
June 29th, 2017
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Situational awareness - The perception of elements in the environment within a given time and space, the comprehension of their meaning, and the projection of their status in the near future (Endsley, 1995)
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AnalyticsAn app to provide context and a frame of reference for disease surveillance information about an unfolding event, through matching of user input to a library of global historical disease outbreaks. (http://aido.bsvgateway.org)
An analysis tool to provide early warning or detection of the re-emergence of an infectious disease at the global level, but through a regional lens. Facilitates long term public health planning.
DatabasesA tool to; a) facilitate obtaining disease surveillance information.Contains information on disease surveillance resources worldwide, b) rapidly select appropriate epidemiological models for infectious disease prediction, forecasting and monitoring. (http://brd.bsvgateway.org)
A data collection and visualization tool for notifiable disease data from around the world. (https://epiarchive.bsvgateway.org)
Epi Archive
LANL’s suite of decision support tools for biosurveillance
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• BRD, EPI Archive and RED Alert are funded by the Defense Threat Reduction Agency (DTRA)• CB 10027
• AIDO was developed with DTRA funds and is currently being enhanced through Department of Homeland Security (DHS) funding• DTRA - CB 10027• DHS - HSHQPM-16-X-00226
Funding support for BSV tools
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Slide 5LA-UR-17-24721• Decision support tools offered through LANL’s Biosurveillance (BSV) gateway
(http://bsvgateway.org/) and DTRA’s Biosurveillance Ecosystem (BSVE)
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• Two functionalities• Understand your unfolding outbreak – analytic components• Learn about representative global historic outbreaks – library
• Contextualizes user input of an unfolding infectious disease outbreak using historical outbreak data
• Places a frame of reference for where a case count is during an outbreak
• Suggests additional information sources that could support effective consequence management of an outbreak
• Provides short term forecasts for unfolding situation using method of analogs
• Provides structured and categorized information about historical outbreaks
Analytics for Investigation of Disease Outbreaks
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Understanding an unfolding outbreak
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Understanding an unfolding outbreak
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Understanding an unfolding outbreak
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Summary information included for every outbreak record in AIDO
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Browsing the library
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Browsing the library
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Interagency collaborations through AIDO (DTRA-DHS)
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• Expand AIDO to include priority diseases for the National Biosurveillance Integration Center (NBIC)
• Expand existing AIDO outbreak library for NBIC selected diseases (9)
• Develop an anomalous event detecting algorithm for AIDO
• Refine AIDO UI and functionality to meet NBIC needs
• Operational evaluation and demonstration within the APEX program
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An analysis tool that can be used to provide early warning or detection of the re-emergence of an infectious disease at the global level, but through a local lens
– Facilitate future planning of control and preventive measures– Alert a user of local re-emergence of a disease and provide
possible causes – Facilitate hypothesis generation and therefore early
warning/detection for global re-emergence
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What is re-emergence?
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• A user is asked to: – put in a case count or time series of a country-level
event/outbreak– add in the location of the outbreak, followed by the auto-
population of some data – select a historic duration of time to compare local
disease incidence (e.g. 2000-2014)
RED Alert: About the tool
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• RED Alert will:“Local”
– Calculate historical trend/pattern analyses of disease incidence for the location of interest (temporal context)
– Provide information about related countries (those with similar disease incidence) for geospatial context
– Determine a subset of likely associated causes of re-emergence event through machine-learning algorithms (data-driven approach)
“Global”– Provide interactive visuals of the global context which users can use to
generate hypotheses of global re-emergence (VBI)
• RED Alert will not:– Provide insight for outbreak investigation of an ongoing event– Provide a user with direct, categorical classification of an event as “re-
emergent” or not (YET)
RED Alert: About the tool
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• Ease of use• Minimal user input required
• Trend analysis is not readily available elsewhere• In disease incidence (historical populations/geographic
components)
• Exportable graphics and data• Easy to add to reports/for documentation
• “Related indicators” offers informative statistical analysis without modeling/forecasting
• Data can be used (open source APIs) for user’s own models• Facilitating analysis for decision support
RED Alert: Key features
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RED Alert: About the tool
User input panel
Multiple-tabbed interface
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Algorithm 1: Does my outbreak represent a re-emergence event?
User input transformed to disease incidence
Example: Measles in USA 2014, temporal incidence trends
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Algorithm 1: Does my outbreak represent a re-emergence event?
Example: Measles in USA 2014, spatial/geographic incidence trends
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Herd Immunity(coverage >95%)
Adequate vaccination coverage
P (no)
Terminus:Not re-emergence
P (yes)
Majority of infected children
below vaccination age
Adequate vaccine availability
P (no)P (yes)
Majority of infected children too young for second dose of
vaccine
P (yes)
Terminus:re-emergence
P (yes)P (no)
Pockets of unvaccinated communitiesP (no)
Civilian cause (religious, social
motivation)
Terminus:re-emergence
P (yes)
P (no)
Terminus:re-emergence
Terminus:re-emergence
P (yes) P (no)
Terminus:re-emergence
1 2
3
54
1
Algorithm 2: What are associated causes of re-mergence in my location?
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Measles re-emergence component causes
Host
Lack of herd immunity Children below vaccination agePeople receiving only MCV1 not MCV2Low vaccination coverage (specific communities or nationally)
Vaccine failure
Vaccine strategy (number of doses, age of vaccination) Vaccine stability and immunogenicityHandling and administration factors (injection technique, cold chain issues)Vaccine failed to elicit immune response
Weakened immunity HIV positive infants too young to be vaccinated
Pathogen New serotype Replacement of endemic strains with new strainsIntroduction of new strains to non endemic regions
Environment
Inadequate public health infrastructure
Health system deficiencyPoor surveillance
Nosocomial transmission
Natural Environment Weather patternNatural disaster
Human behavior
Mistrust of healthcare/mis-informationMigration (voluntary or involuntary)Mass gatheringsCivil unrest/warCultural practices
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Algorithm 3: Is my outbreak connected to other recent ones and if so, is this global re-emergence?
Visualization of various disease relevant data layers that may connect global outbreaks
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Mr. Esteban AbeytaMr. Derek AberleMrs. Emily Alipio-Lyon Ms. Lauren CastroMs. Maneesha ChitanvisMrs. Ashlynn DaughtonMr. Eric Generous Dr. Geoffrey FairchildMrs. Attelia HollanderMrs. Leslie NaranjoDr. Reid PriedhorskyMr. William RosenbergerMrs. Nileena VelappanDr. Alina Deshpande
LANL BSV tool team
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Thank you!