BioPortal: Disease and Bioagent Information Sharing, Surveillance, Analysis, and Visualization

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BioPortalDisease and Bioagent Information
Sharing, Surveillance, Analysis, and Visualization

• Research Team
• University of Arizona
• University of California, Davis
• Kansas State University
• Arizona Department of Public Health
• University of Utah
• New York State Department of Health/HRI
• California Department of Health Services/PHFE
• U.S. Geological Survey
• The SIMI Group
• Acknowledgements NSF, ITIC, DHS, DOD/AFMIC,
  IDIWC, AZDPS

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Research Partners and Supports

• University of Arizona
• University of California, Davis
• Kansas State University
• University of Utah
• Arizona Department of Public Health
• New York State Department of Health/HRI
• California Department of Health Services/PHFE
• U.S. Geological Survey
• The SIMI Group

• NSF
• CIA/ITIC
• DHS
• DOD/AFMIC
• CDC
• AZDPS

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UA Team Members

• Dr. Hsinchun Chen
• Dr. Daniel Zeng
• Lu Tseng
• Cathy Larson
• Kira Joslin
• Wei Chang
• James Ma
• Hsinmin Lu
• Ping Yan
• Aaron Sun

• Keith Alcock
• Sapna Brahmanandam
• Milind Chabbi
• Yuan Wang

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Outline

• Project Background
• BioPortal V1.0 Achievements
• System Architecture
• System Functionalities
• BioPortal Collaboration Framework
• New Developments
• International Foot-and-mouth Disease Monitoring
• Syndromic Surveillance
• Livestock Health Surveillance

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BioPortal Background
Acknowledgment NSF, ITIC, NYSDH, CDHS,
USGS (Drs. Kvach and Ascher)
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Background (I)

• In September, 2002, representatives of 18
  different agencies, including DOD, DOE, DOJ, DHS,
  NIH/NLM, CDC, CIA, NSF, and NASA, were convened
  to discuss disease surveillance.
• An interagency working group called Disease
  Informatics Senior Coordinating Committee (DISCC)
  was established.
• DISCC established an Infectious Disease
  Informatics Working Committee (IDIWC) to survey
  the field and identify gaps.
• IDIWC developed requirements for a National
  Infectious Disease Informatics Infrastructure
  (NIDII).

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Background (II)

• In June, 2003, IDIWC was charged with the task of
  developing one or more rapid prototype systems to
  demonstrate interoperability and innovation
  across species and jurisdictions.
• Botulism and West Nile virus were selected as
  diseases.
• States of New York and California were selected
  as partners.
• The University of Arizona was chosen as
  integrator and was provided with a supplement to
  an existing NSF grant.

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BioPortal Project Goals

• Demonstrate and assess the technical feasibility
  and scalability of an infectious disease
  information sharing (across species and
  jurisdictions), alerting, and analysis framework.
• Develop and assess advanced data mining and
  visualization techniques for infectious disease
  data analysis and predictive modeling.
• Identify important technical and policy-related
  challenges in developing a national infectious
  disease information infrastructure.

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BioPortal V1.0 Accomplishments

• Prototype system design and development
• Initial design and implementation of
  interoperable messaging backbones
• Live prototype systems
• Preliminary user evaluation
• Information sharing
• Data sharing agreements/memoranda of
  understanding (MOUs) developed
• Many disease datasets integrated into the portal
• Analysis and visualization
• Hotspot analysis research
• Spatial-Temporal Visualizer (STV)

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Information Sharing Infrastructure Design
Portal Data Store (MS SQL 2000)
Data Ingest Control Module Cleansing /
Normalization
Info-Sharing Infrastructure
Adaptor
Adaptor
Adaptor
SSL/RSA
SSL/RSA
XML/HL7 Network
PHINMS Network
New
NYSDOH
CADHS
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Data Access Infrastructure Design
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BioPortal Collaboration Framework

• A Memorandum of Understanding (MOU) is used to
  document the relationship between parties that
  will be sharing data
• Who the entities are and how they will act
  independently and cooperatively
• What the mutual interests, benefits, and purposes
  of sharing data are
• How each party will maintain control over and
  share their resources, and what each party shall
  provide to the other (e.g., system accounts,
  portal access)
• Which types of data are to be shared (e.g., dead
  bird surveillance)

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Summary of MOU

• Confidentiality
• Data is not to be shared outside of the project.
• Data is to be returned or destroyed after 5
  years.
• Ownership
• Original data is owned by providers.
• Data analysis is jointly owned.
• Scope
• Specific diseases are listed.
• Additional diseases can be added.
• Parties agree separately on which data elements
  can be shared (e.g., species, gender, etc.)
• Purpose
• Data may be used for system development, for
  example.

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Datasets Integrated WNV, BOT
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Communications/Messaging

• Scalable, flexible, light-weight, and extendible.
  Easy to include
• New diseases
• New jurisdictions
• New techniques!
• Messaging infrastructure installed and tested
• NYSDOH-UA PHIN MS
• CADHS-UA Regional message broker
• NWHC-UA PHIN MS
• XML generation/conversion
• NY_DeadBird, NY_Alerts, NY_BotHuman, NY_WNVHuman,
  NY_CaptiveAnimal, NY_Mosquito
• CA_BotHuman, CA_WNVHuman, CA_DeadBird,
  CA_Chicken, CA_Mosquito
• USGS_Epizoo

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BioPortal Research Framework

• BioPortal Demo Develop the system for
  demonstration purposes using scrubbed data.
  Refine system functionality and performance based
  on user feedback.
• BioPortal Operation Develop the system for
  production mode with real data and real users.
• BioPortal Research Continue to develop
  advanced technologies and practical sharing
  policies. Expand to new diseases and
  jurisdictions.

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BioPortal Prototype Systems
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Spatio-Temporal Data Mining Hotspot Analysis

• A hotspot is a condition indicating some form of
  clustering in a spatial and temporal distribution
  (Rogerson Sun 2001 Theophilides et al. 2003
  Patil Tailie 2004).
• For WNV, localized clusters of dead birds
  typically identify high-risk disease areas
  (Gotham et al. 2001).
• Automatic detection of dead bird clusters using
  hotspot analysis can help predict disease
  outbreaks and aid in effective allocation of
  prevention/control resources.

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Existing Hotspot Analysis Approach SaTScan

• The spatial scan statistical techniques
  implemented in SaTScan are widely used to detect
  and evaluate disease outbreaks (Kulldorff 2001).
• NYSDOH has used SaTScan to develop an early
  warning system for WNV (Gotham et al. 2001).
• An important factor considered by spatial scan
  statistical analysis is the baseline.
• The significance of the density of dead birds
  depends on the historical distribution of bird
  deaths, human population, and so on.

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Other Hotspot Analysis Approaches CrimeStat and
RSVC

• Hotspot analysis techniques applied to crime
  analysis CrimeStat (Levine 2002).
• CrimeStats Risk-Adjusted Nearest Neighbor
  Hierarchical Clustering (RNNH) Uses a kernel
  density estimation obtained from baseline data to
  adjust the threshold that controls whether data
  points can be grouped together.
• Risk-Adjusted Support Vector Machine Clustering
  (RSVC) It combines the power and flexibility of
  support vector machine-based clustering and the
  risk adjustment idea of RNNH.

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Case Study (NY WNV)

• On May 26, 2002, the first dead bird with WNV was
  found in NY
• Based on NYs test dataset

140 records
224 records
March 5
May 26
July 2
new cases
baseline
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Dead Bird Hotspots Identified
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Hotspot Analysis Findings

• RSVC delivers similar recall levels and higher
  precision than SaTScan.
• RNNH matches RSVC precision, but has very low
  recall.
• RSVC significantly outperforms other methods in
  the F-measure.
• Techniques could be complementary for different
  hotspot analysis tasks.

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Spatial-Temporal Visualization

• Integrates four visualization techniques
• GIS View
• Periodic Pattern View
• Timeline View
• Central Time Slider
• Visualizes the events in multiple dimensions to
  identify hidden patterns
• Spatial
• Temporal
• Hotspot analysis
• Phylogenetic (planned)

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BioPortal HotSpot Analysis RSVC, SaTScan, and
CrimeStat Integrated (first visual, real-time
hotspot analysis system for disease surveillance)

• West Nile virus in California

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Hotspot Analysis-Enabled STV
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BioPortal New Developments

• NSF Infectious Disease Informatics Grant (2004-9)
• International Foot-and-Mouth Disease BioPortal
  (2005-6) FMD Lab, UC Davis
• Human Syndromic Surveillance System Arizona
  State Department of Health (2005-6)
• Livestock Syndromic Surveillance System Kansas
  State University RSVP-A (2005-6)

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New Research Directions

• Analytical Algorithms
• Prospective hotspot analysis auto baseline
  discovery
• Spatial-Temporal correlation analysis
• Dynamic Network Analysis
• Visualization
• International FMD news visualization
• Phylogenetic Spatial-Temporal visualization
• Syndromic Surveillance
• Syndromic surveillance system survey
• Emergency room chief complaint syndromic
  classification
• Livestock syndromic surveillance

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Extended BioPortal Research Framework

• BioPortal Demo
• BioPortal Operation
• BioPortal Research
• FMD BioPortal A dedicated instance of
  BioPortal customized for International
  Foot-and-Mouth disease monitoring. Additional
  functionalities such as gene sequence analysis
  and FMD News are added
• BioPortal Syndromic Surveillance A specialized
  BioPortal instance that processes chief
  complainants using a hybrid method of ontology
  and knowledge rules
• BioPortal Livestock A BioPortal instance
  devoted in Livestock syndromic surveillance case
  management and data analysis

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International FMD BioPortal
Acknowledgment DHS, DOD, UC Davis (Drs. Thurmond
and Lynch)
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Introduction

• Foot-and-mouth disease (FMD) is the top disease
  on the Office International des Epizooties (OIE)
  List A, which can infect all cloven-hoofed
  animals.
• FMD is the most contagious infectious diseases of
  livestock animals
• Massive shedding of virus and contamination of
  the environment.
• Transmitted by direct or indirect contact
  (droplets), animate vectors (humans), inanimate
  vectors (vehicles
• Serologically diverse with seven distinct types
  (A, O, C, SAT1, SAT2, SAT3, Asia1), which makes
  diagnosis and vaccination problematic, and
  genetic diversity likely.
• Endemic in Africa, Asia, Middle East and South
  America
• Potential cost for U.S. outbreaks gt10 billion
• Broader economic impact trade and travel
  restrictions.

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FMD Information Model
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International FMD BioPortal Goals

• Real-time, web-based situational awareness of FMD
  outbreaks worldwide through the establishment of
  an international information sharing and analysis
  system
• FMDv characterization at the genomic level
  integrated with associated epidemiological
  information and modeling tools to forecast
  national, regional, and/or international spread
  and the prospect of import into the U.S. and the
  rest of North America
• Web-based crisis management of resourcesfacilitie
  s, personnel, diagnostics, and therapeutics

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Research Plans

• Global FMD epidemiological data
• (Near) real-time data collection
• Web-based information sharing and analysis
• International FMD news
• Indexed collection of global FMD news
• Search and visualization of the FMD news via the
  web
• FMD genetic/sequence data
• Predictive model using phylogenetic, spatial, and
  temporal information to stop FMD at the boarder
• Visualization for FMD event in time, space, and
  genetic space

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Preliminary Global FMD Dataset

• Provider UC Davis FMD Lab
• Information sources reference labs and OIE
• Coverage 28 countries globally
• Time span May, 1905 March, 2005
• Dataset size 30,000 records of which 6789
  records are complete
• Host species Cattle, Caprine, Ovine, Bovine,
  Swine, NK, Elephant, Buffalo, Sheep, Camelidae,
  Goat

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Global FMD Coverage in BioPortal
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FMD Migration Visualization using BioPortal
(cases in South Asia)
FMD Cases travel back and forth between countries
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International FMD News

• Provider UC Davis FMD Lab
• Information sources Google, Yahoo, and open
  Internet sources
• Time span Oct 4, 2004 present (real-time
  messaging under development)
• Data size 460 events (6/21/05)
• Coverage 51 countries
• (Africa11, Asia16,
• Europe12, Americas12)

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Searching FMD News

• http//fmd.ucdavis.edu/
• Searchable by
• Date range
• Country
• Keyword

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Visualizing FMD News on BioPortal
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FMD Genetic Information Analysis

• Genome clustering analysis
• Phylogenetic clustering
• Spatial clustering
• Temporal clustering
• Hotspot detection among gene sequences
• Create a tree structure based on semantic
  distance between gene sequences.
• Automatically detect the dense portion of the
  tree.
• Identify the connection between the semantic
  cluster and the geographic pattern of gene
  sequences.

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FMD Genetic Visualization

• Goal Extend STV to incorporate 3rd dimension,
  phylogenetic distance
• Include a phylogenetic tree.
• Identify phylogenetic groups and color-code the
  isolate points on the map.
• Leverage available NCBI tools such as BLAST.
• Proof of concept SAT 2 3 analysis
• Data 54 partial DNA sequence records in South
  Africa received from UC Davis FMD Lab
  (Bastos,A.D. et al. 2000, 2003)
• Date range 1978-1998
• Countries covered South Africa, Zimbabwe,
  Zambia, Namibia, Botswana

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Sample FMD Sequence Records
Color-coded View (MEGA3)
Textual View of Gene Sequence
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Phylogenetic Trees
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Interactive Phylogenetic Tree
Color coding shows similarity of sequences
User-adjustable grouping threshold to change
clusters
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Phylogenetic Treeof Sample FMD Data
Identify 6 groups within 2 major families (MEGA3
based on sequence similarity)
Group6
Group1
Group2
Group5
Group4
Group3
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Genetic, Spatial, and Temporal Visualization of
FMD Data
Phylogenetic tree color coded
Isolates locations color coded
Isolates appearances in time
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FMD Time Sequence Analysis
First family cases appeared throughout the period
2nd family cases exist before 1993 and a comeback
lately
Second family cases existed before 1993 and
reappeared later after 1997
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FMD Periodic Pattern Analysis
2nd family concentrated in Feb. while 1st family
spread evenly
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Locations of Family 1 records
Selected only groups 1, 2, and 3 and found a
spatial cluster
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Locations of Family 2 records
Sparse isolate locations
Selected only groups 4, 5, and 6
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Syndromic Surveillance

• What is syndrome?
• Syndrome is a group of symptoms which shows
  possibility of a certain disease
• What is syndromic surveillance?
• The term syndromic surveillance applies to
  surveillance using health-related data that
  precede diagnosis and signal a sufficient
  probability of a case or an outbreak to warrant
  further public health response.
• Why use syndromic surveillance?
• Traditional disease surveillance systems mainly
  rely on physicians to report reportable
  diseases occurrences. This process is too
  lengthy and does not leave enough response time.

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Syndromic Surveillance System Survey
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Syndromic SurveillanceData Sources
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Syndrome Classification

• To tag emergency room chief complaints with
  syndrome label
• EX coughing with high fever gt upper
  respiratory
• Challenges
• Free text no standard language. Ex chst pn,
  CP, c/p, chest pai, chert pain, chest/abd pain,
  chest discomfort all mean chest pain
• Very short usually no more than 3 words
• Multiple languages
• Existing approaches
• Rule-based classification (EARS)
• Bayesian Net classification (RODS)

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Proposed Syndrome Classification
Symptom Groups
CC Cleansing
EMT-P
Chief Complaints
UMLS hierarchy
UMLS concepts
Rule processing engine (defined by public health
officers)
fever
Upper Respiratory Syndrome
cough
If Fever and (cold or cough) -gtthen upper
respiratory
UMLS Unified Medical Language System
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Kansas RSVP-A System
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Rapid Syndrome Validation Project Animal
(RSVP-A) System

• URL http//clh.vet.ksu.edu
• Main function allows vets to enter syndromic
  observations and retrieve statistics bar charts
• A complete system with administrative functions
  such as profile editing
• Provides 2 web-based interfaces
• Regular browser
• Mobile devices (WAP)
• Current users 17 vets in 29 counties
• Projected users 200 (Kansas), 10K (nationwide)

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BioPortal Integration Livestock
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Data Characteristics

• Time Period
• 7/16 2003 10/17 2005
• Cross 2 states/29 counties in Kansas and New
  Mexico

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Imported Attributes

• RSVP-A monitors 6 syndromes
• Non-neonatal diarrhea,
• Neurologic / recumbant
• Unexpected deaths
• Weight loss/feed refusal
• Abortion/birth defect
• Erosive lesions

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Records
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STV Map View

• Geocoded at county level

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Seasonal Effect

• Tuesdays and Fridays have high volumes

• Dec and Jan have higher volume

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BioPortal Surveillance Future Work

• Complete systems comparison and user evaluation
• Multi-lingual chief complaints tagging and
  analysis
• Establish real-time data messaging
• Integrate potential new datasets
• Plant disease surveillance in Great Plains
• Hanta virus in South America

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BioPortal Future Work

• Complete open source, generic BioPortal
  architecture and system
• Develop multi-lingual BioPortal
• Incorporate other diseases, e.g., avian
  influenza, SARS
• Solicit partners and expand test sites
• Continue infectious disease informatics research

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For more informationBioPortal web site
http//www.bioportal.orgAI Lab web site
http//ai.arizona.eduhchen_at_eller.arizona.edu