Geovisualization of COVID-19: State of the Art and Opportunities

RÉSUMÉ:La cartographie de la prévalence et de la propagation des maladies infectieuses n'a jamais été plus cruciale que dans le contexte de la pandémie de COVID-19. Une pléthore de tableaux de bord de SIG en ligne incorporant la fonctionnalité SIG de base ont été créés; ces tableaux de bord ont servi de plateforme pour le partage rapide de données et la communication d'information en temps réel, facilitant somme toute la prise de décisions. Toutefois, bon nombre de ces tableaux ont été axés uniquement sur la présentation et le contrôle de l'incidence cumulative ou quotidienne des données sur la COVID-19, sans égard à la dimension temporelle. Les auteurs se penchent sur l'utilité des tableaux de bord basés sur les SIG pour cartographier la prévalence de la COVID-19, mais également sur les occasions manquées de mettre l'accent sur le composant temporel de la maladie (cyclicité, saisonnalité). Ils évoquent la possibilité d'un recours aux techniques avancées de géovisualisation pour intégrer le composant temporel aux cartes animées interactives illustrant a) le risque relatif quotidien et le nombre de jours pendant lesquels une zone géographique a été un foyer de contagion, b) le ratio du nombre de cas observés par rapport au nombre de cas prévus dans le temps et c) la dynamique du nombre des décès dans un cube espace-temps. Les auteurs illustrent ces méthodes au moyen des cas de COVID-19 et du nombre des décès aux États-Unis, à l'échelon des comtés, entre le 25 janvier et le 1er octobre 2020. Ils expliquent comment chacune de ces méthodes de visualisation peut faciliter la compréhension d'importants concepts de santé publique appliqués à la pandémie comme le risque, la propagation et le taux de mortalité. Enfin, les auteurs proposent des pistes à envisager pour promouvoir la recherche au carrefour de la visualisation spatiotemporelle et des maladies infectieuses.ABSTRACT:Mapping the prevalence and spread of infectious diseases has never been more critical than during the COVID-19 pandemic. A plethora of Web-based GIS dashboards have been created that incorporate basic GIS functionality; these dashboards have served as platforms for rapid data sharing and real-time information, ultimately facilitating decision making. However, many of them have merely focused on presenting and monitoring cumulative or daily incidence of COVID-19 data, disregarding the temporal dimension. In this paper, we review the usefulness of GIS-based dashboards for mapping the prevalence of COVID-19, but also missed opportunities to emphasize the temporal component of the disease (cyclicity, seasonality). We suggest that advanced geovisualization techniques can be used to integrate the temporal component in interactive animated maps illustrating (a) the daily relative risk and the number of days a geographic region has been in a disease cluster, (b) the ratio between the observed and expected number of cases over time, and (c) mortality count dynamics in a space–time cube. We illustrate these approaches by using COVID-19 cases and death counts across the U.S. at the county level from 25 January 2020 to 1 October 2020. We discuss how each of these visualization approaches can promote the understanding of important public health concepts applied to the pandemic such as risk, spread, and mortality. Finally, we suggest future avenues to promote research at the intersection of space–time visualization and infectious diseases.

[1]  B. Napoletano,et al.  Spatial analysis and GIS in the study of COVID-19. A review , 2020, Science of The Total Environment.

[2]  A. Griffin Trustworthy maps , 2020, J. Spatial Inf. Sci..

[3]  Daniel A. Griffith,et al.  Space-time cluster detection with cross-space-time relative risk functions , 2020, Cartography and Geographic Information Science.

[4]  Robert E Roth,et al.  Value-by-alpha Maps: An Alternative Technique to the Cartogram , 2010, The Cartographic journal.

[5]  Kristin A. Cook,et al.  Illuminating the Path: The Research and Development Agenda for Visual Analytics , 2005 .

[6]  Ingemar J. Cox,et al.  Digital technologies in the public-health response to COVID-19 , 2020, Nature Medicine.

[7]  HeerJeffrey,et al.  D3 Data-Driven Documents , 2011 .

[8]  M. J. Kraak,et al.  Epidemics and pandemics in maps – the case of COVID-19 , 2020 .

[9]  J. Mennis Mapping the Results of Geographically Weighted Regression , 2006 .

[10]  Vaishnavi Thakar,et al.  Unfolding Events in Space and Time: Geospatial Insights into COVID-19 Diffusion in Washington State during the Initial Stage of the Outbreak , 2020, ISPRS Int. J. Geo Inf..

[11]  P. von Dadelszen,et al.  Is the closest health facility the one used in pregnancy care-seeking? A cross-sectional comparative analysis of self-reported and modelled geographical access to maternal care in Mozambique, India and Pakistan , 2020, International Journal of Health Geographics.

[12]  E. Taioli,et al.  Bivariate Spatial Pattern between Smoking Prevalence and Lung Cancer Screening in US Counties , 2020, International journal of environmental research and public health.

[13]  Eric M. Delmelle,et al.  Visualizing the impact of space-time uncertainties on dengue fever patterns , 2014, Int. J. Geogr. Inf. Sci..

[14]  E. Delmelle,et al.  Space-Time Visualization of Dengue Fever Outbreaks , 2016 .

[15]  Song Gao,et al.  Mapping county-level mobility pattern changes in the United States in response to COVID-19 , 2020, SIGSPATIAL Special.

[16]  D. Buckeridge,et al.  SeroTracker: a global SARS-CoV-2 seroprevalence dashboard , 2020, The Lancet Infectious Diseases.

[17]  Heidrun Schumann,et al.  Space, time and visual analytics , 2010, Int. J. Geogr. Inf. Sci..

[18]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[19]  Menno-Jan Kraak,et al.  The space - time cube revisited from a geovisualization perspective , 2003 .

[20]  E. Dong,et al.  An interactive web-based dashboard to track COVID-19 in real time , 2020, The Lancet Infectious Diseases.

[21]  J. Merelo,et al.  DatAC: A visual analytics platform to explore climate and air quality indicators associated with the COVID-19 pandemic in Spain , 2020, Science of The Total Environment.

[22]  Anthony C. Robinson A Design Framework for Exploratory Geovisualization in Epidemiology , 2007, Inf. Vis..

[23]  Bruce Houghton,et al.  The Lancet Infectious Diseases , 2003 .

[24]  Alan M. MacEachren,et al.  Geographic visualization: designing manipulable maps for exploring temporally varying georeferenced statistics , 1998, Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258).

[25]  Carson Sievert Interactive Web-Based Data Visualization with R, plotly, and shiny , 2020 .

[26]  Charles Hansen,et al.  The Visualization Handbook , 2011 .

[27]  Alan M. MacEachren,et al.  A Comparison of Animated Maps with Static Small-Multiple Maps for Visually Identifying Space-Time Clusters , 2006 .

[28]  Mark Monmonier,et al.  How to Lie with Maps, Third Edition , 2018 .

[29]  Eric Delmelle,et al.  Computationally Enabled 4D Visualizations Facilitate the Detection of Rock Fracture Patterns from Acoustic Emissions , 2018, Rock Mechanics and Rock Engineering.

[30]  M. J. Pereira,et al.  Geostatistical COVID-19 infection risk maps for Portugal , 2020, International Journal of Health Geographics.

[31]  Wayne W. Eckerson Performance Dashboards: Measuring, Monitoring, and Managing Your Business , 2005 .

[32]  Clayton Williams,et al.  Data collection and communications in the public health response to a disaster: rapid population estimate surveys and the Daily Dashboard in post-Katrina New Orleans. , 2007, Journal of public health management and practice : JPHMP.

[33]  Marc Levoy,et al.  Display of surfaces from volume data , 1988, IEEE Computer Graphics and Applications.

[34]  Carsten Juergens,et al.  Trustworthy COVID-19 Mapping: Geo-spatial Data Literacy Aspects of Choropleth Maps , 2020, KN - Journal of Cartography and Geographic Information.

[35]  Wenwu Tang,et al.  Accelerating the discovery of space-time patterns of infectious diseases using parallel computing. , 2016, Spatial and spatio-temporal epidemiology.

[36]  J. Everts The dashboard pandemic , 2020 .

[37]  Tomoki Nakaya,et al.  Visualising Crime Clusters in a Space‐time Cube: An Exploratory Data‐analysis Approach Using Space‐time Kernel Density Estimation and Scan Statistics , 2010, Trans. GIS.

[38]  Jingyuan Zhang,et al.  Geo-visualization and Clustering to Support Epidemiology Surveillance Exploration , 2010, 2010 International Conference on Digital Image Computing: Techniques and Applications.

[39]  Cynthia A. Brewer,et al.  Basic mapping principles for visualizing cancer data using Geographic Information Systems (GIS). , 2006, American journal of preventive medicine.

[40]  Cynthia A. Brewer,et al.  Beyond Graduated Circles: Varied Point Symbols for Representing Quantitative Data on Maps , 1998 .

[41]  Michael R. Desjardins,et al.  A space–time parallel framework for fine-scale visualization of pollen levels across the Eastern United States , 2018, Cartography and Geographic Information Science.

[42]  Applications of GIS and geospatial analyses in COVID-19 research: A systematic review. , 2020, F1000Research.

[43]  Edward Angel,et al.  Interactive Computer Graphics with WebGL , 2014 .

[44]  E. Delmelle,et al.  Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States , 2020, Spatial and Spatio-temporal Epidemiology.

[45]  Suleman Sarwar,et al.  COVID-19 challenges to Pakistan: Is GIS analysis useful to draw solutions? , 2020, Science of The Total Environment.

[46]  Yuhao Kang,et al.  Mapping county-level mobility pattern changes in the United States in response to COVID-19 , 2020, ArXiv.

[47]  M. Monmonier How to Lie with Maps , 1991 .

[48]  Erik Duval,et al.  Learning Analytics Dashboard Applications , 2013 .

[49]  Torsten Hägerstraand WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .

[50]  Wenwu Tang,et al.  Spatiotemporal Point Pattern Analysis Using Ripley's K Function , 2017 .

[51]  L. Thorpe,et al.  City-Level Measures of Health, Health Determinants, and Equity to Foster Population Health Improvement: The City Health Dashboard , 2019, American journal of public health.

[52]  J. Flannery THE RELATIVE EFFECTIVENESS OF SOME COMMON GRADUATED POINT SYMBOLS IN THE PRESENTATION OF QUANTITATIVE DATA , 1971 .

[53]  Sara Irina Fabrikant,et al.  The Role of Map Animation for Geographic Visualization , 2008 .

[54]  Daniel W. Archambault,et al.  Animation, Small Multiples, and the Effect of Mental Map Preservation in Dynamic Graphs , 2011, IEEE Transactions on Visualization and Computer Graphics.

[55]  Joann M. Taylor,et al.  Digital Color Imaging Handbook , 2004 .

[56]  James W Hardin,et al.  Diabetes and the socioeconomic and built environment: geovisualization of disease prevalence and potential contextual associations using ring maps , 2011, International journal of health geographics.

[57]  Claus Rinner,et al.  Evaluating web-based static, animated and interactive maps for injury prevention. , 2009, Geospatial health.

[58]  Edzer J. Pebesma,et al.  Multivariable geostatistics in S: the gstat package , 2004, Comput. Geosci..

[59]  Lydia R. Lucchesi,et al.  Visualizing uncertainty in areal data with bivariate choropleth maps, map pixelation and glyph rotation , 2017 .

[60]  J. Crampton,et al.  Geographies of the COVID-19 pandemic , 2020, Dialogues in Human Geography.

[61]  Ian Welch,et al.  Survey on geographic visual display techniques in epidemiology: Taxonomy and characterization , 2020, J. Ind. Inf. Integr..