The document discusses a framework for leveraging geospatial web services in public health to improve data sharing, surveillance, and outbreak management. It emphasizes the need for a collaborative information ecology, utilizing Service-Oriented Architecture (SOA) and grid technologies to enable flexible and scalable access to health data while maintaining local control. Challenges and solutions related to interoperability, data privacy, and user accessibility are also addressed to foster better collaboration and efficiency in public health initiatives.

1. A Framework for Geospatial Web Services for Public Health June 8, 2009 URISA Public Health Conference Leslie Lenert, MD, MS, FACMI, Director National Center for Public Health Informatics, CCHIS, CDC

2. Our Latest Enemy (Ally?)

3. National Trends in ILI Data from BioSense

4. Percent ILI Reported to BioSense by Federal Region, 10/04/2008 to 05/30/2009

5. But really, don’t we all want this?

6. Our Goal Should Tracking and Forecasting Outbreak Progression to Improve Control GLEaMviz.org

7. State of Public Health Surveillance: An Information Supply Chain Incentives for sharing are not strong Incentives for holding on to data are strong People providing data do not have access to all the information. CDC State, County, and Local Health Departments Laboratories Practitioners

8. Future Model: Information Ecology Leverage Existing Capacity Distribute resources and infrastructure Increase flexibility and scalability Provide Local Control of data and services Reduces political barriers Address many privacy concerns Foster Collaboration to define requirements, priorities, develop, and deploy technology Users / Experts Standards, Services, Guidance Local/ State Data Local/State Health Dept Surveillance & Informatics Capacity Analysis / Visualization Capacity National Data Academic / Industry Partners Local/ State Data CDC & Other Federal Agencies Scientific and Public Health Priorities Public Health Grid

9. What is Service Oriented Architecture? “ A paradigm for organizing and utilizing distributed capabilities that may be under the control of different ownership domains.” – OASIS Reference Model for Service Oriented Architecture 1.0, CS 1, 19 July 2006 1. Loosely coupled, modular service components Not a new concept (e.g. decades of mainframe services, and programmed service components) Business process centric (not data centric) Standards based interoperability Delegation of functional responsibilities across multiple systems Modular development of applications, as collections of service oriented business components Collaborative, multidisciplinary, distributed SOA Principles Modularity, componentization, reuse, granularity, interoperability Compliance with standards (both common and industry-specific) Service provisioning (description, semantics, visibility, service interface, contract & policy, execution context), consumption, choreography, auditing and governance 1. OASIS is the Organization for the Advancement of Structured Information Standards

10. Implications of SOA – Massimo Pezzini

11. Implications of SOA – Massimo Pezzini

12. Implications of SOA – Massimo Pezzini

13. Business Process as Services Process definition is essential for service creation Integrated Business & Technology View Aligned to Lines of Business Not necessarily “web services” Delivers return through agility, not just reuse Technology becomes the “easy” part of SOA Theo Beack - http://theobeack.typepad.com/technology/2006/02/soa_approaches_.html

14. Implications of SOA – Dan Ellis

15. Why is SOA Important? SOA is a fundamental shift in information systems development, comparable to the shift from mainframe to client-server computing. – Gartner Agility, adaptability Ready data sharing across diverse partners and technical platforms Increased manageability and scalability Rapid application development to address new demands Application assembly; composition of shared services via portals Distributed workload, lower cost Synergy of distributed services development (e.g. WWW) Major industry trend IT middleware platform evolution Powerful application architectures (e.g. Google, eBay, Amazon,…)

16. Implications of SOA Technical challenges Application/services integration middleware technologies, atop the application server (e.g. ESB, Service Directory & Repository, Orchestration, Automated message routing/brokering, Transformation, etc.) Escalating technical complexity and cost with use of what Gartner calls “SOA Backplane” technologies Management complexity of service orchestration and Event-Driven Architectures Layered service architecture topologies Solutions (presentation & dialog) Process Services (orchestration layer) Core Business Services Layer Underlying Services Utility Services Paradigm shift in systems design and development practices Multi-disciplinary collaborations among varied disciplines and skills (EA, KM, Information Architecture, Business Intelligence, BPM) Designing to distribution on a global grid (network) of shared services, across diverse partners and unanticipated consumers Supporting reliable services assembly, orchestration and execution in unanticipated patterns (e.g mashups)

17. An Example SOA Governance

18. Source: http://www.fgdc.gov/training/nsdi-training-program/materials/IntroGeoBusinessPlanning.ppt#328,7,Geospatial Profile of the FEA Geospatial Line of Business and Federal Enterprise Architecture FEDERAL OMB pressure for Consolidation of GIS Resources and Shared Services

19. GRID Concept A computing infrastructure… Expands circle of trusted resources outside of the organization Provides dependable, consistent, pervasive and inexpensive access to data and applications. By pooling assets into a virtual system, a grid allows data owners to share data and applications across boundaries while maintaining control.

20. Types of Grids Computational Grids Focused on the application of several computers to a single problem at the same time – usually to a scientific or technical problem that requires a great number of computer processing cycles or access to large amounts of data. (virtual super-computer) Collaboration / Access Grids Focused on the collection of resources and technologies that enables large format audio and video based collaboration between groups of people in different locations. Data Grids Focused on he controlled sharing and management of large amounts of distributed data. Dynamic combinations or all on same platform

21. Computational Grids Most famous/infamous A massively distributed computing environment composed of over 3 million Internet-connected computers launched in May 1999—has led to a unique public involvement in science. Three million PCs deliver 6,000 CPU years per day—the fastest (admittedly special-purpose) computer in the world

22. @Home Model Extended Grid application models protein folding & misfolding (1224 teraflops , as of 23 Sept 2007) Grid application models the way malaria spreads in Africa and the potential impact that new anti-malarial drugs may have on the region Grid application models the design of new anti-HIV drugs based on molecular structure ( in silico ) 1

23. Collaboration Grid Example Presentation, visualization and interactive environments Runs on the same grid as the computational grid These combined resources are used to support group-to-group interactions, large-scale distributed meetings, collaborative work sessions, seminars, lectures, tutorials, and training

24. Data Grid Examples caBIG – Cancer Research Datagrid GEON – Geosciences Network Datagrid EGEE / CERN - The world's largest particle physics laboratory... where the web was born (LHC – The Large Hadron Collider, May, 2008) DataGrid –EU funded resource of shared large-scale databases TeraGrid –Shares resources at San Diego Supercomputer Center, Indiana University, Oak Ridge National Laboratory, National Center for Supercomputing Applications, Pittsburgh Supercomputing Center, Purdue University, Texas Advanced Computing Center, University of Chicago/Argonne National Laboratory, and the National Center for Atmospheric Research

25. Commercial Grid Products Tier 1 Tier 2

26. Open Source Grid Software/Projects

27. Commercial Grid “Consumers” Amazon Elastic Compute Cloud & Amazon Simple Storage Service

28. Future Model: Information Ecology Leverage Existing Capacity Distribute resources and infrastructure Increase flexibility and scalability Provide Local Control of data and services Reduces political barriers Address many privacy concerns Foster Collaboration to define requirements, priorities, develop, and deploy technology Users / Experts Standards, Services, Guidance Local/ State Data Local/State Health Dept Surveillance & Informatics Capacity Analysis / Visualization Capacity National Data Academic / Industry Partners Local/ State Data CDC & Other Federal Agencies Scientific and Public Health Priorities Public Health Grid

29. Grid as a supporting technical framework for public health… Lets experts retain stewardship of information Flexibility allows Integration, Interoperability & Data Access between Silos In the long-term the Cost & Time to Re-engineer Existing App Silos falls Other Public Health Application Silos Epi-X BioSense

31. Examining PHGrid and Security: Leveraging the Expertise of Others… Grid Authentication and Authorization with Reliably Distributed Services (GAARDS) is a series of tools developed by Ohio State to enhance the open source Gobus Toolkit. Provides enterprise level administrative tools for managing users, federated identities, trust, credential delegation, group management, access control policy, and integration between grid and non-grid-based security domains.

32. BioSurveillance POC: Federated Search Primary requirements Demonstrate the capability to share and visualize biosurveillance data: Within a State Between States Between States and CDC Aggregate data under control of state, share results with external users Combine and visualize results in the form of maps and simple analysis (e.g. Epi Curve) Goal: Explore standards based federated frameworks to promote distributed data stewardship, analytical access, and collaboration between participating stakeholders. Inform NCPHI and its public health and commercial partners of best practices and potential issues to this approach, and provide a foundation to evaluate existing and emerging interoperability protocols.

33. RODSA-DAI Foundation: Real Time Outbreak Detection System and Globus Grid Toolkit RODS - ~20 production instances across US Globus – Leading Open Source Grid Middleware; used in NCI’s caBIG, GeonGrid Hypothesis: Extending RODS with Globus Services allows the ability to query across installations, and visualize data from disparate / secured nodes

34. Poison Control Data Access & Integration Demonstrate access and visualization of poison control call data via web services Display data over multiple days over multiple call classifications Combine and visualize results in the form of maps and simple charts Goal: Research ability to augment public health situational awareness, by accessing non-clinical data sources of public health importance, based on secure web services

35. Poison Control Data Access & Integration

36. Linking GIS and non GIS sources: GIPSE (Geocoded Interoperable Population Summary Exchange) Format Obtain consensus on most relevant elements Create common biosurveillance data structure aligning to AHIC / HITSP standards Develop interfaces to existing partner biosurveillance systems Distribute & refine using open source principles Proposed elements Geo region Zip, zip3, etc. Condition Condition classifier Count Date range Cross tabs for age category, sex Goal: Facilitate multi-state public health situational awareness with simple, common data interchange service based on a subset of key biosurveillance data elements

37. GIPSE is a set of matrixes Date range Zip codes 1,2,3,5,0,6,… 2,1,4,7,0,3,… … . … . … . counts Influenza Gastrointestinal Rash

38. GIPSE+ adds cross tabs for age category and gender Age category Date range Zip codes Sex Date range Zip codes + Influenza cases

39. GIPSE SERVICES REGISTRY

40. Clinical or Surveillance Database GIPSE Data set cache Periodic Extract of GIPSE data sets Result #1 GIPSE Delivery Service (Grid node) GIPSE Publication Service Result #2 Result #3 Firewall GIPSE Registry Quicksilver Viewer

41. PHGrid / NHIN Interoperability Research Goal: Leverage investments in quality assessment for public health Demonstrate that the NHIN and PHGrid are complimentary and compatible platforms for distributed services and federated data Demonstrate that the NHIN and PHGrid are capable of trusting data from each other Challenges with bidirectional communication between the two environments: Differences in the level of security of data that resides in each environment Differences in the core functions of each platform

42. How does GIS fit into this? By providing Visualization services Spatial analytic services Geospatial data services Modeling services Task services (routing, geocoding,etc) Integration of geocoded and location coded data

43. Geospatial Services GIS community has been using web services for about 10 years through image and feature services using products such as ArcIMS. GIS Portals such as Geography Network, Geospatial One Stop, and a number of other geospatial portals have provided one stop shopping for data services similar to web service registries. There are currently thousands a geospatial services across the global GIS community but many of these are not being leveraged as well as they should. The public health community has lagged behind other communities such as defense, natural resources, and transportation in using and sharing these services to support program initiatives. Because the public health community has lagged behind communities they have not been able to leverage the financial and intellectual investments of their partners there re-creating similar efforts across programs at all levels of public health.

44. PH-DGInet To help the geospatial community leverage each other investments CDC has been exploring the use of the Distributed Geospatial Intelligence Network (DGInet) but modifying it for public health DGInet is a GRID consisting of geospatial services in the defense/intel community. DGInet consists of: Services Oriented Architecture (SOA)/ open standards based portal environment Designed for non-GIS savvy military planners and intelligence analysts Scaleable/N-Tier/Backward Compatible/C2B-B2B Architecture Designed to the lowest common denominator Server side operations 30 nodes across the globe Public Health DGInet Currently has 3 nodes (CDC, ESRI, and the South Carolina Department of Health and Environment) Can create a distributed aggregated choropleth map and histrogram from across nodes in real time. Can be used to discover geospatial services across the network to use in application development or desktop analysis

45. PH-DGINet: Enabling shareable GIS services Distributed/shared web services framework based on ESRI GIS technology PH-DGINet allows Access to data, services and tools available within a single portal per organization PH-DGInet portal can be customizable to organization, role, individual DGInet supports discretionary service and data sharing PH-DGINet can be extended with simple analytical tools Exploring service integration / interoperability with Globus stack

46. ArcIMS (10 years of this) DATABASE DATABASE MAP SERVER LAN ArcIMS Consumers

47. PH-DGInet WAN (Multiple PH-DGInet nodes) LAN PH-DGInet SERVER CDC PH-DGInet Node LAN PH-DGInet Consumers (Internet Explorer) (PH-DGInet Explorer) (ArcGIS Client) LAN PH-DGInet SERVER EPA PH-DGInet Node LAN LAN PH-DGInet SERVER CA PH-DGInet Node LAN LAN PH-DGInet SERVER NIH PH-DGInet Node LAN

48. Aggregation Query: State accessing Veterans Admin Data at CDC PH-DGINet South Carolina Consumers DGInet SERVER South Carolina PH-DGINet Node Event Database DGInet SERVER CDC PH-DGINet Node Event Database DGInet SERVER Other State PH-DGINet Node Event Database Access Row Level Data Aggregate Row Level Data (Web Client) SQL Query Aggregated VA Data

49. Aggregation Query: State access it’s own hospital data and VA data at CDC PH-DGINet South Carolina Consumers DGInet SERVER South Carolina PH-DGINet Node Event Database DGInet SERVER CDC PH-DGINet Node Event Database DGInet SERVER Other State PH-DGINet Node Event Database Access Row Level Data Aggregate Row Level Data Access Row Level Data Aggregate Row Level Data (Web Client) SQL Query Aggregated VA Data

50. PH-DGInet Architecture All open source software except ArcGIS Server Only need a browser to discover geospatial data, map it, and do geospatial analytics At the same time, GIS Analyst can leverage the same services in their desktops for additional analysis and developers can leverage the same services to build applications.

51. PH-DGINet: Increased Interoperability July 23, 2009 Draft Clients DGINet Services Data Service Providers OGC Map Viewers DGINet Map Viewer ArcGIS Desktop ArcGIS Explorer Commercial Map Viewers Google Earth ArcGIS Server WMS Server GML Server WFS Server KML Server ArcIMS Server DGINet Web Services - GetMetadataService -LocatorService -GetListOfProductNamesService -GetProductService -GetFeaturesService -GetMapImage Service -GetAnnotationService -GetExtractService -DGINetSystemServiceBroadcastService -OGCProvidersManagerService -SystemMonitorService -DGINetSystemServiceUDDIService -DGINetSystemServiceScheduleService -DGINetSystemServiceNotifyService -LOS_GeoprocessingService DGINet Tools - Bookmark -Download/zip -Annotation -Query Builder Data Management Service DGINet Content Manager API Custom HTML Web Pages DGINet -CMS

52. 3-D Visualization through a browser

53. States currently exploring PH-DGInet

54. PH-DGInet Screens

55. PH-DGInet Toolbar

56. PH-DGInet Geospatial Data Nodes

57. PH-DGInet Geoprocessing Sevices

58. Adding in an open source (OGC) web service

59. Create and Maintain Bookmarks

60. Toolbox – NPDS Query National Poison Control Data Service Basemaps are coming from Redlands, California and the Poison Control data is coming from Denver. The poison control data is only a data service, not a feature service so PH-DGInet is building the spatial component on the fly based on the geography listed in the data.

61. Tool – Aggregation Query Select data from multiple nodes that will build one choropleth map and one histrogram from data from multiple nodes, in real time. Background map layer comes from Redlands, California, hospital data comes from South Carolina, and the VA data comes from CDC So three nodes are used to make this one map

62. Summary SOA and Grid will help the public health community share services to support application development and analysis but at the same time reduce cost and share investments Help create an information ecology The data stay at the source so the currency of the data is up to the data provider, not a clearinghouse Quality and collaboration Grid technologies enhance the access to geospatial SOA service registry in PH-DGInet will provide a common place to access and consume these services Geospatial SOA and Grid computing will allow both non-savvy GIS users to consume and view the data through the PH-DGInet viewer but at the same time, the GIS analysts can consume and use the same services in ArcGIS desktop for analysis

63. Questions?