Visualizing public health data for communicable disease management - - PowerPoint PPT Presentation

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Visualizing public health data for communicable disease management - - PowerPoint PPT Presentation

Nov 25, 2022 340 likes •759 views

Visualizing public health data for communicable disease management and control Anamaria Crisan PhD Candidate, Computer Science The University of British Columbia Data visualization in the GenEpi current paradigm = Communication of scientific

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Visualizing public health data for communicable disease management and control

Anamaria Crisan

PhD Candidate, Computer Science The University of British Columbia

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Data visualization in the GenEpi current paradigm

  • f scientific research

= Communication

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Do you have an

Outbreak? Yes. No.

Do all the

Science!

But you want to

Monitor

right?

Duh. Inform

the masses!

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https://www.ratbotcomics.com/comics/pgrc_2014/1/1.html

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Do you have an

Outbreak? Yes. No.

Do all the

Science!

But you want to

Monitor

right?

Duh. Inform

Maybe data

Visualization? Infographics are pretty

the masses!

4

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SLIDE 5

Do you have an

Outbreak? Yes. No.

Do all the

Science!

But you want to

Monitor

right?

Duh. Inform

Did it work? Maybe data

Visualization?

the masses!

5

Infographics are pretty

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Do you have an

Outbreak? Yes. No.

Do all the

Science!

But you want to

Monitor

right?

Duh. Inform

Did it work? Maybe data

Visualization? No : (

the masses!

Different Infographics?

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Do you have an

Outbreak? Yes. No.

Do all the

Science!

But you want to

Monitor

right?

Duh.

the masses!

Inform

Did it work? Maybe data

Visualization? No : ( Different Infographics? Declare Victory Yes! (maybe?)

7

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Data Visualization!

  • Many different visualization designs
  • Design impacts data interpretation
  • How to choose which is best?
  • Feelings (ad hoc)
  • Impressions (ad hoc)
  • Systematic assessment (lacking)

Challenge : Multiple Alternatives

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www.vishealth.org

OPTION A OPTION B OPTION C

  • Same objective (understand treatment efficacy), same data, different visualizations
  • Tested accuracy, timeliness, and preference with 2,038 participants
  • Option A was most accurate, easier (faster) to read, and preferred

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Challenge : Multiple Alternatives

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Challenge : Multiple Alternatives

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Lack of Systematic Thinking about Data Visualization

§ Peer-reviewed, systematic approaches

§ Automated systems & packages

§ Benchmark comparisons / evaluation § Attempts to standardize § Formal instruction § Community dialogue

§ papers, reviews, blog posts

§ Ad hoc – visualization mainly by intuition, trial & error

§ Some automated systems & packages

§ No real comparison / evaluation § No attempts to standardize § No formal instruction § Some community dialogue

§ blog posts (kind of), twitter

Bioinformatics Methods GenEpi Data Visualization

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Introducing

GEviT

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Introducing GEviT

§ GEviT = Genomic Epidemiology Visualization Typology

§ A way to describe data visualization for analysis § Organizes qualitative descriptors into a typology § What does GEviT do and not do?

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Introducing GEviT

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§ GEviT = Genomic Epidemiology Visualization Typology

§ A way to describe data visualization for analysis § Organizes qualitative descriptors into a typology § What does GEviT do and not do? Evaluation Systematic Preliminary GEviT provides a base

§ Deliverables : 1. Typology 2. Interactive Gallery

GEviT does not evaluate

§ Massive undertaking that would take many years § Needs GEviT to conduct evaluations

Completion: Fall 2017 Completion: Fall 2117?

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Introducing GEviT

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§ GEviT = Genomic Epidemiology Visualization Typology

§ A way to describe data visualization for analysis § Organizes qualitative descriptors into a typology § What does GEviT do and not do? Evaluation Systematic Preliminary GEviT provides a base

§ Deliverables : 1. Typology 2. Interactive Gallery

GEviT does not evaluate

§ Massive undertaking that would take many years § Needs GEviT to conduct evaluations

Completion: Fall 2017 Completion: Fall 2117?

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Introducing GEviT

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Systematic Preliminary GEviT provides a base

§ Deliverables : 1. Typology 2. Interactive Gallery

Completion: Fall 2017

§ GEviT = Genomic Epidemiology Visualization Typology

§ A way to describe data visualization for analysis § Organizes qualitative descriptors into a typology § What does GEviT do and not do? Evaluation GEviT does not evaluate

§ Massive undertaking that would take many years § Needs GEviT to conduct evaluations

Completion: Fall 2117?

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How does GEviT do this?

§ Create a why-what-how typology

§ Uses methods from qualitative methods & infovis research § Typology, not ontology : typologies are lighter weight than ontologies

§ May be used in conjunction with a ontology § Blame epistemologists for their systems of classification

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How does GEviT do this?

§ Create a why-what-how typology

§ Uses methods from qualitative methods & infovis research § Typology, not ontology : typologies are lighter weight than ontologies

§ May be used in conjunction with a ontology § Blame epistemologists for their systems of classification

§ Why are data being visualized?

§ i.e. show transmission in a hospital

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How does GEviT do this?

§ Create a why-what-how typology

§ Uses methods from qualitative methods & infovis research § Typology, not ontology : typologies are lighter weight than ontologies

§ May be used in conjunction with a ontology § Blame epistemologists for their systems of classification

§ Why are data being visualized?

§ i.e. show transmission in a hospital

§ What data are being visualized?

§ i.e. patient location, duration in hospital, test outcomes, SNPs, clusters

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How does GEviT do this?

§ Create a why-what-how typology

§ Uses methods from qualitative methods & infovis research § Typology, not ontology : typologies are lighter weight than ontologies

§ May be used in conjunction with a ontology § Blame epistemologists for their systems of classification

§ Why are data being visualized?

§ i.e. show transmission in a hospital

§ What data are being visualized?

§ i.e. patient location, duration in hospital, test outcomes, SNPs, clusters

§ How are data being visualized?

§ i.e. timeline, phylogenetic tree (high-level) § i.e. test outcome = shape ; patient location = colour; cluster = spatial arrangement (low-level)

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Development

GEviT

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GEviT Development (Example)

OPTION A OPTION B

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WHY: Show within hospital transmission

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OPTION A OPTION B

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HO HOW (hi high-le level) l): Ti Timeline HO HOW (hi high-le level): ): Phylogeny HO HOW (hi high-le level) l): Ti Timeline HO HOW (hi high-le level): ): Node-lin link graph

GEviT Development (Example)

Same why, different high-level how

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OPTION A OPTION B

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WHAT: Lo Location [ [ HOW: Co Colour ] WHAT: Lo Location [ [ HOW : Co Colour ]

GEviT Development (Example)

Same why, same what , same how

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OPTION A OPTION B

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WH WHAT: Test Performed [ [ HOW : Glyph ] WH WHAT: Test Performed [ HOW : Li Line ] WH WHAT: Test Result [ [ HOW : Co Colour]

GEviT Development (Example)

Same why, sameish what , different how

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OPTION A OPTION B

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WH WHAT: Clusters [ [ HOW : Co Colour ]

GEviT Development (Example)

Same why, different what and how

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OPTION A OPTION B

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GEviT Development (Example)

WH WHAT: Tran ansmission Confidence [ [ HOW: Co Colour ]

Same why, different what and how

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What How Options

High-level Low-level High-Level Low-level A B Admin Patient ID Timeline Annotation X X

  • P. Sample ID

Annotation, Colour X Genomic SNP Distance Phylogeny Annotation, Position X Clusters Position, Colour X Spatial Location Timeline Colour X X Laboratory Test Performed Timeline Glyph X Line, Colour X Test Result Colour X Temporal Admission Date Timeline Position X X Episode Duration Bar X X Test Date Position X X Transmission Transmission Confidence Node-link graph Colour X

How is what you see

GEviT Development (Example)

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What How Options

High-level Low-level High-Level Low-level A B Admin Patient ID Timeline Annotation X X

  • P. Sample ID

Annotation, Colour X Genomic SNP Distance Phylogeny Annotation, Position X Clusters Position, Colour X Spatial Location Timeline Colour X X Laboratory Test Performed Timeline Glyph X Line, Colour X Test Result Colour X Temporal Admission Date Timeline Position X X Episode Duration Bar X X Test Date Position X X Transmission Transmission Confidence Node-link graph Colour X

GEviT Development (Example)

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What How Options

High-level Low-level High-Level Low-level A B Admin Patient ID Timeline Annotation X X

  • P. Sample ID

Annotation, Colour X Genomic SNP Distance Phylogeny Annotation, Position X Clusters Position, Colour X Spatial Location Timeline Colour X X Laboratory Test Performed Timeline Glyph X Line, Colour X Test Result Colour X Temporal Admission Date Timeline Position X X Episode Duration Bar X X Test Date Position X X Transmission Transmission Confidence Node-link graph Colour X

GEviT Development (Example)

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What How Options

High-level Low-level High-Level Low-level A B Admin Patient ID Timeline Annotation X X

  • P. Sample ID

Annotation, Colour X Genomic SNP Distance Phylogeny Annotation, Position X Clusters Position, Colour X Spatial Location Timeline Colour X X Laboratory Test Performed Timeline Glyph X Line, Colour X Test Result Colour X Temporal Admission Date Timeline Position X X Episode Duration Bar X X Test Date Position X X Transmission Transmission Confidence Node-link graph Colour X

GEviT Development (Example)

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What How Options

High-level Low-level High-Level Low-level A B Admin Patient ID Timeline Annotation X X

  • P. Sample ID

Annotation, Colour X Genomic SNP Distance Phylogeny Annotation, Position X Clusters Position, Colour X Spatial Location Timeline Colour X X Laboratory Test Performed Timeline Glyph X Line, Colour X Test Result Colour X Temporal Admission Date Timeline Position X X Episode Duration Bar X X Test Date Position X X Transmission Transmission Confidence Node-link graph Colour X

GEviT Development (Example)

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What How Options

High-level Low-level High-Level Low-level A B Admin Patient ID Timeline Annotation X X

  • P. Sample ID

Annotation, Colour X Genomic SNP Distance Phylogeny Annotation, Position X Clusters Position, Colour X Spatial Location Timeline Colour X X Laboratory Test Performed Timeline Glyph X Line, Colour X Test Result Colour X Temporal Admission Date Timeline Position X X Episode Duration Bar X X Test Date Position X X Transmission Transmission Confidence Node-link graph Colour X

GEviT Development (Example)

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What is GEviT’s status?

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Systematic Portion of Project Anticipation Completion: Fall 2017

Prototype Gallery: https://amcrisan.shinyapps.io/gevit_gallery_prototype/

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GEviT Takeaways

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Data Visualization!

§ Data visualization rigor is possible § Rigor is also desirable

§ Analytic, rather than ad hoc, process to data visualization

§ Rigor will be necessary

§ Clinical application of GenEpi § Ensuring visualization is interpretable

§ GEviT provides a basis

§ Evolving framework § Enables evaluation

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The Bigger Picture

Autodesk Research (2017). Same Stats, Different Graphs: https://www.autodeskresearch.com/publications/samestats

Same stats, different graphs

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Thanks! Contact

@amcrisan <slide url> http://cs.ubc.ca/~acrisan

  • Dr. Gardy, Dr. Munzner, &

the UBC infovis group

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Extra Slides

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Do all the

Science! Data Visualization!

the masses!

Inform

Missed EDA Opportunity

  • EDA = Exploratory Data Analysis
  • Incorporate data visualization as a

part of scientific process

  • Science != just statistics + wetlab
  • Visualization != just pretty pictures
  • Visualization can …
  • Check methdological assumptions
  • Show unanticipated patterns
  • Generate new hypothesis

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How does GEviT add Rigor to Data Visualization in GenEpi?

§ Ad hoc – visualization mainly by intuition, trial & error § No real comparison / evaluation § No attempts to standardize § No formal instruction § Some community dialogue

GEviT introduces design alternatives, systematic reasoning GEviT enables comparison for design & evaluation GEviT standardizes descriptions of elements in visualization design GEviT bridges GenEpi with infovis pedagogy GEviT intended to start a bigger dialogue about data visualization practices

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Design Trajectory of GEviT Gallery:

EXAMPLE: Info about vis EXAMPLE: Info about vis

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Treevis.net Setviz.net Vishealth.org