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 research 2
Inform Do all the Yes. Science! the masses ! Do you have an Outbreak? Duh. But you No. want to Monitor right? 3 https://www.ratbotcomics.com/comics/pgrc_2014/1/1.html
Inform Do all the Yes. Science! the masses ! Infographics are pretty Do you have an Outbreak? Duh. Maybe data Visualization? But you No. want to Monitor right? 4
Inform Do all the Yes. Science! the masses ! Infographics are pretty Do you have an Outbreak? Duh. Maybe data Did it Visualization? work? But you No. want to Monitor right? 5
Inform Do all the Yes. Science! the masses ! Different Infographics? Do you have an Outbreak? Duh. No : ( Maybe data Did it Visualization? work? But you No. want to Monitor right? 6
Inform Do all the Yes. Science! the masses ! Different Infographics? Do you have an Outbreak? Duh. No : ( Maybe data Did it Visualization? work? But you No. want to Monitor Yes! right? (maybe?) Declare Victory 7
Challenge : Multiple Alternatives Many different visualization designs • Design impacts data interpretation • Data How to choose which is best? • Feelings (ad hoc) • Visualization! Impressions (ad hoc) • Systematic assessment (lacking) • 8
Challenge : Multiple Alternatives 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 9 www.vishealth.org
Challenge : Multiple Alternatives 10
Lack of Systematic Thinking about Data Visualization Bioinformatics Methods GenEpi Data Visualization § Peer-reviewed, systematic § Ad hoc – visualization mainly by approaches intuition, trial & error § Automated systems & packages § Some automated systems & packages § Benchmark comparisons / evaluation § No real comparison / evaluation § Attempts to standardize § No attempts to standardize § Formal instruction § No formal instruction § Community dialogue § Some community dialogue § papers, reviews, blog posts § blog posts (kind of), twitter 11
Introducing GEviT 12
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? 13
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? GEviT does not evaluate GEviT provides a base § Massive undertaking that would take § Deliverables : many years 1. Typology 2. Interactive Gallery § Needs GEviT to conduct evaluations Preliminary Systematic Evaluation Completion: Fall 2017 Completion: Fall 2117? 14
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? GEviT does not evaluate GEviT provides a base § Massive undertaking that would take § Deliverables : many years 1. Typology 2. Interactive Gallery § Needs GEviT to conduct evaluations Preliminary Systematic Evaluation Completion: Fall 2017 Completion: Fall 2117? 15
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? GEviT does not evaluate GEviT provides a base § Massive undertaking that would take § Deliverables : many years 1. Typology 2. Interactive Gallery § Needs GEviT to conduct evaluations Preliminary Systematic Evaluation Completion: Fall 2017 Completion: Fall 2117? 16
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 17
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 18
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 19
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) 20
GEviT Development 21
GEviT Development (Example) WHY : Show within hospital transmission OPTION A OPTION B 22
GEviT Development (Example) Same why, different high-level how OPTION A OPTION B HO HOW ( hi high-le level) l): Ti Timeline HOW ( hi HO high-le level) l): Ti Timeline HO HOW ( hi high-le level ): ): Phylogeny HO HOW ( hi high-le level ): ): Node-lin link graph 23
GEviT Development (Example) Same why, same what , same how OPTION A OPTION B WHAT: Lo Location [ [ HOW: Co Colour ] WHAT: Lo Location [ [ HOW : Co Colour ] 24
GEviT Development (Example) Same why, sameish what , different how OPTION A OPTION B WH WHAT: Test Performed [ [ HOW : Glyph ] WH WHAT: Test Performed [ HOW : Li Line ] WH WHAT: Test Result [ [ HOW : Co Colour] 25
GEviT Development (Example) Same why, different what and how OPTION A OPTION B WH WHAT: Clusters [ [ HOW : Co Colour ] 26
GEviT Development (Example) Same why, different what and how OPTION A OPTION B WH WHAT: Tran ansmission Confidence [ [ HOW: Co Colour ] 27
GEviT Development (Example) What How Options High-level Low-level High-Level Low-level A B Annotation X X Patient ID Admin Timeline P. Sample ID Annotation, Colour X SNP Distance Annotation, Position X Genomic Phylogeny Clusters Position, Colour X How is what you see Spatial Location Timeline Colour X X Glyph X Test Performed Laboratory Timeline Line, Colour X Test Result Colour X Admission Date Position X X Temporal Episode Duration Timeline Bar X X Position X X Test Date Transmission Transmission Confidence Node-link graph Colour X 28
GEviT Development (Example) What How Options High-level Low-level High-Level Low-level A B Annotation X X Patient ID Admin Timeline P. Sample ID Annotation, Colour X SNP Distance Annotation, Position X Genomic Phylogeny Clusters Position, Colour X Spatial Location Timeline Colour X X Glyph X Test Performed Laboratory Timeline Line, Colour X Test Result Colour X Admission Date Position X X Temporal Episode Duration Timeline Bar X X Position X X Test Date Transmission Transmission Confidence Node-link graph Colour X 29
GEviT Development (Example) What How Options High-level Low-level High-Level Low-level A B Annotation X X Patient ID Admin Timeline P. Sample ID Annotation, Colour X SNP Distance Annotation, Position X Genomic Phylogeny Clusters Position, Colour X Spatial Location Timeline Colour X X Glyph X Test Performed Laboratory Timeline Line, Colour X Test Result Colour X Admission Date Position X X Temporal Episode Duration Timeline Bar X X Position X X Test Date Transmission Transmission Confidence Node-link graph Colour X 30
GEviT Development (Example) What How Options High-level Low-level High-Level Low-level A B Annotation X X Patient ID Admin Timeline P. Sample ID Annotation, Colour X SNP Distance Annotation, Position X Genomic Phylogeny Clusters Position, Colour X Spatial Location Timeline Colour X X Glyph X Test Performed Laboratory Timeline Line, Colour X Test Result Colour X Admission Date Position X X Temporal Episode Duration Timeline Bar X X Position X X Test Date Transmission Transmission Confidence Node-link graph Colour X 31
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