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Dracones: Web-Based Mapping and Spatial Analysis for Public Health Surveillance

Dracones: Web-Based Mapping and Spatial Analysis for Public Health Surveillance. Christian Jauvin David Buckeridge McGill University. Summary. Dracones: Built with MapServer/PostGIS We'll be covering: Public Health context Software architecture Some specific problems.

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Dracones: Web-Based Mapping and Spatial Analysis for Public Health Surveillance

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  1. Dracones: Web-Based Mapping and Spatial Analysis for Public Health Surveillance Christian Jauvin David Buckeridge McGill University

  2. Summary • Dracones: • Built with MapServer/PostGIS • We'll be covering: • Public Health context • Software architecture • Some specific problems

  3. Public Health - Two Perspectives • Case management • Individual cases of notifiable diseases • Relationship networks • Population surveillance • Larger risk patterns

  4. Case Management • Questions/problems: • Is a case due to recent transmission? • If so, does the case share any feature with other, recent cases? • Ways it's being done: • Investigations/interviews • Meeting with other investigators

  5. Population Surveillance • Questions/problems: • Are more cases happening than expected? • Does an excess suggest ongoing transmission in a specific region? • Way it's being done: • Semi-automated routine temporal and space-time statistical analysis (SaTScan)

  6. Montreal DSP • Département de santé publique de Montréal (Public Health Agency) • Need: incorporate spatial data + analysis capabilities within workflow • One reason: research shows that spatial information helps • Answer: Dracones project • Funded in part by GeoConnections • Led by David Buckeridge, MD, PhD • 15 month contract

  7. Case Management at the DSP • Current Situation • Information on paper entered into system (Oracle DB + Forms) • System contains sensitive data (names, addresses) • Limited tools for analyzing case data • Project Goal • Capture spatial data • Visualize and analyze spatial distribution of cases

  8. Population Surveillance at the DSP • Current Situation • Routine temporal and space-time statistical analysis • Capacity to visualize time-series but not maps • Project Goal • Add mapping capacity • Extend range of analytic methods

  9. Why Location Matters - Case Management • If you are studying a case of a certain disease that was just declared • It is harder to picture the situation by looking at something as this..

  10. Why Location Matters - Case Management

  11. Why Location Matters - Case Management • Than by looking at this..

  12. Why Location Matters - Case Management

  13. Why Location Matters - Population Surveillance • If you are studying the spatial distribution of a set of disease clusters • This would seem more difficult..

  14. Why Location Matters - Population Surveillance

  15. Why Location Matters - Population Surveillance • Than this..

  16. Why Location Matters - Population Surveillance

  17. Development Process • Management Team • Led by public health MD with informatics training • Members from each area of DSP involved • User Involvement • Users on management team • Input throughout requirements, design, development

  18. Software Required and Our Choices

  19. Web Architecture Benefits • Usually lighter/simpler technologies • Cross-platform • Ease of deployment and integration • Builds on existing set of conventions and behaviours

  20. System Architecture Dracones Current Case Management System Python R SaTScan { Web client Oracle Forms { Apache + PHP MapServer + MapScript Bridge Oracle DB PostgreSQL/PostGIS DB

  21. Client Side - UI • UI is 100% Javascript (ExtJS library) • Future project: extract the map-manipulation parts: • Tile-based panning • Zooming • Layer activation And releasing them under an OS license

  22. Client Side - Functions • From the results of a query performed in the Oracle client, launch the application to visualize the results • Inspect those results by varying certain parameters • Launch external analysis tools

  23. Server Side - MapServer • MapServer: OS tool that add geospatial content to web applications • Can be used as a CGI • Interface with many programming languages • Works very closely with PostGIS

  24. Server Side - MapServer • MapServer with Apache 2.2, using PHP5 • Linux and Windows • Since it's stateless, each interaction: • Build a map object from a base mapfile • Modify the map object (according to client parameters) • Return rendered map as a file to the client (that will display it)

  25. MapServer - Layers • A map object is made of layers • A layer can be loaded from a shapefile (ESRI open format), that specifies its geometry • Or it can be loaded directly from a PostGIS table

  26. PostGIS • PostGIS: spatial extension for PostgreSQL • Adds geometry types (points, lines, polygons, etc) • Spatial functions and operators (distance, convex hull, intersection, etc) • Spatial indexes

  27. PostGIS • Queries that mix spatial and non-spatial aspects of the data • If you have a case table:

  28. PostGIS And a region table:

  29. PostGIS You can then build a query like this: SELECT * FROM case, region WHERE case.condition = 'TB' AND case.region_id = region.id AND within(region.geom, GeomFromText('POLYGON(…)')

  30. PostGIS • A MS layer can be built simply by adding a connection attribute, pointing to the PG table (two lines really!) • Shapefile and table sources can be mixed

  31. Analysis Tools - SaTScan • Requirement: interfacing with analysis tools • SaTScan: detection of space-time clusters • Scan for areas where the probability of being a case is significantly higher than being a non-case

  32. Analysis Tools • Since it's a command-line tool without an open API, we use Python to run it, parse the results and plot them using MapServer • We do the same for some external R routines

  33. System Data Sources • Health data • Reportable disease database • Ancillary data on contacts • Geographical data • Street networks and postal code file • Health regions, census, postal boundaries

  34. Address: 1500-a Sherbroooke St. Ouest Using Address Data from a Public Health Database • Problem: addresses are stored as character fields: • No validation at the entry point • Data quality is compromised

  35. Two Problems with Address Processing • The addresses need to be parsed, and possible (and numerous) transcript errors and ambiguities must be solved • The ones which refer to a same place must be identified and treated as a unique object

  36. Possible Solutions • These could be solved in a more SQL-integrated manner: edit distance module for PG (?) • We decided however to go the procedural way (using Python)

  37. Address Validation Algorithm - Requirements • A database with (1) the street network geometry • (2) the street segment address ranges • And (3) the postal code geometry and street range association

  38. H2X2T1 2001 H2X2T2 1001 1998 3001 Sherbrooke Street 998 Sherbrooke Street 2998 Address Validation Algorithm So you will know for instance that:

  39. Address Validation Algorithm - Steps • Parse the text addresses in 3 tokens: • {S#, SN, PC} • For each triplet: • Try to find an exact match, by being tolerant on SN (maximum coverage, edit distance..) • By being tolerant on SN, try to vary PC • Idem with SN, fix PC and vary S#

  40. Address Validation Algorithm - Batch Results • By doing a batch analysis of the DSP data (105K records), we found that: • 84% of the address records were "exact" • 14.5% were recoverable errors • 1.5% were non-recoverable errors

  41. H2X2T1 2001 H2X2T2 1001 1998 3001 Sherbrooke Street 998 Sherbrooke Street 2998 1500 Sherbrooke Last Address Processing Step: Geocoding Geocoding by interpolation:

  42. A Last Problem • DSP management system is read-only (for us) • Not spatially enabled • Must not affect performance

  43. And its Solution • Create a mirror of the DSP data model, using PG • Augmented with spatial aspects (and more adapted address handling) • Refreshed periodically • Reprocessing of the content that has changed • Extraction of the new one

  44. A Challenge • Interface and extend existing: • System • Environment (including an important community of users and developers)

  45. Lessons Learned • Very strong interest in using spatial information at the DSP but infrastructure, skills and data quality are limiting • Large effort to validate and correct all addresses • The science of spatial analysis in public health often lags the technology • How to analyze multiple locations for each individual? • How important is spatial location in an urban area? • Open-source, web-based mapping software and spatial databases (MapServer, PostGIS) are robust and easy to work with for skilled developers

  46. Acknowledgements • GeoConnections, CIHR • McGill University • Aman Verma, Sherry Olsen, Andrew Carter • Montreal DSP • Louise Marcotte • Robert Allard, Lucie Bedard, André Bilodeau • Montreal Chest Institute • Kevin Schwartzman, Jonathan Richard • Alice Zwerling, Marie-Josee Dion

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