Using two of the VAST 2010 mini challenges as a case study, we report on the design decisions and software development process used to create visual analytics software for understanding disease spread and mutation. The software we developed and the analysis conducted attempted to help us understand (a) how a fictitious disease may have spread between selected cities around the globe; and (b) how genetic sequences taken from infected patients may be used to chart the evolution of the disease and changes in its severity, drug resistance and other characteristics

Dykes, J.

Slingsby, A.

Wood, J.

English

Jo Wood

Aidan Slingsby

Jason Dykes

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Designing visual analytics systems for disease spread and evolution

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Wood, J., Slingsby, A. & Dykes, J. (2010). Designing visual analytics systems for disease spread and evolution: VAST 2010 mini challenge 2 and 3 award: Good overall design and analysis. Paper presented at the Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium on , 25 - 26 Oct 2010 , Salt Lake City, Utah, US.City Research OnlineOriginal citation: Wood, J., Slingsby, A. & Dykes, J. (2010). Designing visual analytics systems for disease spread and evolution: VAST 2010 mini challenge 2 and 3 award: Good overall design and analysis. Paper presented at the Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium on , 25 - 26 Oct 2010 , Salt Lake City, Utah, US.Permanent City Research Online URL: http://openaccess.city.ac.uk/404/ Copyright & reuseCity  University  London has developed City  Research Online  so that  its  users  may access the research outputs of City University London's staff. Copyright © and Moral Rights for this paper are retained by the individual author(s) and/ or other copyright holders. Users may download and/ or print one  copy  of  any  article(s)  in  City  Research  Online  to  facilitate  their  private  study  or  for  non-commercial research. Users may not engage in further distribution of the material or use it for any profit-making activities or any commercial gain. All material in City Research Online is checked for eligibility for copyright before being made available in the live archive. URLs from City Research Online may be freely distributed and linked to from other web pages. Versions of researchThe version in City Research Online may differ from the final published version. Users are advised to check the Permanent City Research Online URL above for the status of the paper.EnquiriesIf you have any enquiries about any aspect of City Research Online, or if you wish to make contact  with the author(s) of this paper, please email the team at publications@city.ac.uk.Designing Visual Analytics Systems for Disease Spread and EvolutionVAST 2010 Mini Challenge 2 and 3 Award: Good overall design and analysisJo Wood∗ Aidan Slingsby† Jason Dykes‡giCentre (http://gicentre.org), School of InformaticsCity University London1 INTRODUCTIONUsing two of the VAST 2010 mini challenges as a case study, wereport on the design decisions and software development processused to create visual analytics software for understanding diseasespread and mutation. The software we developed and the analy-sis conducted attempted to help us understand (a) how a fictitiousdisease may have spread between selected cities around the globe;and (b) how genetic sequences taken from infected patients may beused to chart the evolution of the disease and changes in its severity,drug resistance and other characteristics.Underlying our approach to the visual analytics process is theidea that tool development and visual analysis using the tool arenot separate processes. As such we do not use off-the-shelf, highlevel visual analytics packages, but instead the lower level program-ming environment Processing [4] – a graphically oriented set ofJava libraries suitable for data visualization [3]. As well as provid-ing the necessary graphics and interaction facilities, Processing isengineered to ease rapid prototyping and can therefore be used foriterative tool development - data analysis tasks.2 DESIGN FOR IDENTIFYING PANDEMIC DISEASE SPREADChallenge MC2 required participants to process nearly 1Gb of textfiles of hospital admission data. Data related to admissions ineleven cities around the world over a ten week period and includedreported symptoms and demographic details of each person admit-ted. The challenge involved characterising a disease and its geo-graphic spread over time. Our approach to building software tomeet this challenge was guided by a number of design principles:• use multiply-coordinated views to afford comparisons be-tween spatial, temporal and thematic patterns;• use layout to maximise the information content of the repre-sentations and afford comparison between them;• allow filtering by day, place and admission summary to enable‘detail on demand’;• pre-compute any measures necessary for rapid interaction;• maximise the data/ink ratio of all visual representation;Our first step was to produce a rapid visualization of the data inorder to get a sense of its volume and diversity of symptoms thatform the basis for analysis. We did this by using Processing to pro-duce tag clouds of symptom terms, alphabetically ordered and sizedby frequency. This revealed that some data cleaning was necessaryto remove errors in the data, normalization of synonyms and stem-ming of verbs and adjectives. Data cleaning was achieved by iter-atively writing cleaning code in Processing and visualizing the re-sults, modifying the code and repeating. Once clean we were able to∗e-mail: jwo@soi.city.ac.uk†e-mail: a.slingsby@soi.city.ac.uk‡e-mail: jad7@soi.city.ac.ukhypothesise disease symptoms by calculating the Chi-expectationvalue [5, 6] for each term by comparing observed fatal symptomswith all symptoms for each hospital. Results were visualised as acolored tag cloud. Storing the results of analytical processing ina database allowed more rapid prototyping and interaction duringsubsequent iterations.Our next iteration of the program design/analysis involved ex-ploring the geographic patterns of the disease. Given its global ex-tent, our initial design involved mapping the measures of the disease(numbers of fatal admissions, numbers of patients with diagnosticsymptoms etc.) to a global map base (see Figure 1 top-left). Rapiddevelopment (about an hour’s programming) was enabled by usingmapping and interaction libraries we had previously developed forother applications [7]. On visualising the results it was apparentthat the geography of the distribution was not as rich as its temporalvariation and that some some form of visual ‘temporal signature’would be useful in detecting change in the pattern of admissionsover time. This led to the construction of histograms of variousdisease measures (vertical axis) over time (horizontal axis). Align-ing them vertically for each of the eleven cities enabled geographiccomparisons of temporal patterns (see Figure 1 right).The challenge required us to assess the possible relationships ofsymptoms with gender and age. We therefore created an expandedhistogram view on which gender and age profiles could be dis-played (Figure 1 bottom-left). To compare with disease symptoms,the final view we constructed provided a list of symptom terms thatcould be ordered by various criteria, such as frequency and TF-IDF.(Figure 1 center). All of the views were linked to allow tempo-ral and spatial filtering (“what happened around the world on thisday?”; “when was the onset and retreat of the disease in this city?”).3 DESIGN FOR IDENTIFYING GENETIC MUTATIONSChallenge MC3 required us to identify genetic sequences that cor-responded to particular disease strains and the mutations that wereassociated with changes in their severity. The numbers of sequencesinvolved and their length required less filtering than MC2 in orderto visualise the data. As such the design of software was guided byuse of alignment and sorting to explore the data. The four geneticbases were symbolised with categorical colors from ColorBrewer[1] using previously written libraries [7]. They were sufficientlyshort to be displayed horizontally with zooming and panning avail-able for detail on demand. Initial visual exploration revealed that allmutations were substitutions, so aligning all bases by their positionin the sequence was possible (if there had been insertions or dele-tions, bases would have to be aligned using common base-patternsinstead). It was also clear that the majority of DNA bases were com-mon to the set of sequences, so these could be removed from viewenabling focus on the mutations between strains (see Figure 2).Two of the challenge questions required sequences to be rankedin order to identify the most likely disease strains that satisfied par-ticular criteria. We reflected this in the design of the interaction byallowing graphical sorting of sequences based on criteria selectedby the user. This allowed common mutations to be juxtaposed insuch a way to allow comparison.Figure 1: Final layout of PandemView showing spatial view (top left), thematic view (center), temporal views by city (right) with grey verticaltime-line centred on 19th May and enlarged temporal view with gender and age profiles (bottom left)Figure 2: Final layout of SequenceView showing native sequences (top), outbreaks (middle) and longest common sequences found in theoutbreaks (bottom). Common sequences are identified in red and those over which the mouse is positioned are colored yellow.4 CONCLUSIONSWe created two bespoke applications suited to the nature of the vi-sual analytics task at hand. Assembling and analysing the data,writing and modifying the applications each took about two person-days, which we regard as being comparable to the amount of timerequired using an off-the-shelf visual analytics toolkit. Unlike theuse of an existing software ‘tool’, a lower-level programming ap-proach affords greater flexibly in the interaction between analystand software [2]. Importantly, it blends the distinction betweensoftware development and data analysis, allowing discoveries madeabout the data to influence the subsequent design of the visual toolsused to explore the data. The cost in this approach to visual ana-lytics is the skillset required to develop the applications. Howeverwith the availability of increasingly usable programming environ-ments such as Processing and data visualization libraries, this is anapproach available to an increasing number of analysts.For detailed answers to the challenge questions and interpreta-tion of the data see http://gicentre.org/vast2010.REFERENCES[1] C. Brewer and M. Harrower. Colorbrewer: Color advice for maps.http://colorbrewer2.org, 2009.[2] J. Dykes. Facilitating interaction for geovisualization. In J. Dykes,A. MacEachren, and M. Kraak, editors, Exploring Geovisualization,pages 265–291. Elsevier, Amsterdam, 2005.[3] B. Fry. Visualizing Data. O’Reilly, Cambridge, 2007.[4] B. Fry and C. Reas. Processing. http://processing.org, 2010.[5] C. R. Unit. People in Britain: a Census Atlas. HMSO, London, 1980.[6] J. Wood, J. Dykes, A. Slingsby, and K. Clarke. Interactive visual explo-ration of a large spatio-temporal dataset: Reflections on a geovisualiza-tion mashup. Transactions on Visualization and Computer Graphics,13(6):1176–1183, 2007.[7] J. Wood, A. Slingsby, and J. Dykes. giCentre utilities.http://gicentre.org/utils, 2010.

Designing visual analytics systems for disease spread and evolution: VAST 2010 mini challenge 2 and 3 award: Good overall design and analysis

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Designing visual analytics systems for disease spread and evolution: VAST 2010 mini challenge 2 and 3 award: Good overall design and analysis

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