Experts agree that access to, sharing, and application of location-enabled information is a key component in addressing health related emergencies. While the present COVID-19 pandemic has underscored a range of successes in dealing with the COVID virus, many gaps in supporting local to global preparedness, forecasting, monitoring, and response have been identified when dealing with a health crisis at such an unprecedented level. This study considers how a common, standardized health geospatial data model, schema, and corresponding spatial data infrastructure (SDI) could establish a blueprint to better align the community for early warning, response to, and recovery from future health emergencies. Such a data model would help to improve support for critical functions and use cases.
II. Executive Summary
Experts agree that access to, sharing, and application of location-enabled information is a key component in addressing health related emergencies. While the present COVID-19 pandemic has underscored a range of successes in dealing with the COVID virus, many gaps in supporting local to global preparedness, forecasting, monitoring, and response have been identified when dealing with a health crisis at such an unprecedented level. A common, standardized health geospatial data model and schema would establish a blueprint to better align the community for early warning, response to, and recovery from future health emergencies. Such a data model would help to improve support for critical functions and use cases.
This Concept Development Study (CDS) aims to engage the health and geospatial communities across industry, government, academia, and research organizations in the evaluation of the current state and future design of a geospatially-enabled Health Data Model and corresponding Health Spatial Data Infrastructure (SDI). To achieve these purposes, this initiative emphasizes the examination of four health related data categories and three health emergency use cases. The results of this initiative include a notional health data model that can be the basis for piloting, prototyping, and use by the global community to improve detection, monitoring, and forecasting. It should also support improved planning, preparedness, response, and recovery for future health emergencies including epidemics / pandemics of infectious as well as environmentally related diseases and other impacts on population health.
A starting assumption of the study was that health information could be usefully organized into four categories including population and patient data; supply chain data; health facilities data; and foundation and contextual data. We also understood the importance of both bio-science data and clinical research data, which although generally not having spatial characteristics, never-the-less determined many aspects of the response to a health disaster, including the development of diagnostics; the design and production of vaccines; and the identification of effective treatments. These six data categories were not challenged and are used as the main building blocks of the health data model.
The CDS has also placed emphasis on the importance of examining the health spatial data value chain. The initiative participants agreed that it should be possible to demonstrate that every component of the data model served important purposes for applications and modeling, actionable intelligence, operations support, and the delivery of health benefits. The participants believe that the value of a data model hinges on the practical utility of the data elements identified.
Request For Information (RFI) responses and expert opinions agreed that many lower income countries lacked basic information about their health facilities and had very limited information about their populations. Often health records for large segments of the population did not exist at all. For these countries progress would entail the initial building of foundational GIS and health information. As a first step it would be important to establish a national basemap within a coordinate reference system, that could be adopted nationally and used by government, the private sector, and volunteer organizations that provide health support and assistance. The common basemap could then be used to identify the location and characteristics for health facilities of all kinds. A number of contributors pointed out that developing a complete inventory of health facilities, and keeping it up to date, would provide major benefits. Other key datasets that could be derived from and represented on a basemap include: the depiction of all transportation networks (roadway, air, rail, water), especially those connecting to health facilities; the identification of population centers ranging from settlements to cities including a focus on slum areas; the identification of clean water and wastewater infrastructure; and supply chains for diagnostics, PPE, medical equipment, vaccines, food, and other essential items. Finally, it was agreed that there should be a health spatial data infrastructure “maturity model” that while starting at the most basic level, provided low and middle income countries with a pathway towards further development and greater usefulness. Responders also agreed that smartphones with GPS could be used to obtain crowdsourced information about health status and local health needs, and could even start to be used as a diagnostic platform.
Responses largely referenced the pandemic use case and focused on the response to COVID-19. Noted was the high rate of viral spread, much of which was asymptomatic, making it very difficult to identify those infected so they could be isolated. Responders also identified the slow deployment of diagnostic tests, delays in getting test results, the slow ramping up of contact tracing operations, the failure to capture precision location information, the inadequacy of supply chains, and the reluctance to share information due to privacy concerns. Among the key recommendations for shaping a health data model included: the development of national address databases and geo-coding applications to ensure that precision addresses of those testing positive could be quickly captured and mapped either to the address point, or rolled up into any geography necessary to support applications and models; the automatic, digital capture of patient information at first interaction with the health system, with that information following the patient through all health system encounters; the compilation and integration of many kinds of demographic, housing, facility and infrastructure information for multiple uses by different segments of the response community; and the development of a geocoding module for contact tracing applications to enable rapid hotspot and micro-cluster identification.
A picture emerged that the cost for making basic health data improvements in countries at all income levels does not need to be great. For lower income countries accurate imagery and the development of a health facilities layer, does not come with a high price tag. For higher income countries, many of which already have spatial enterprise systems, the challenge is to bring together data, much of which already exists, for use in existing applications and models, and to support newer artificial intelligence capabilities. A Health Disaster Pilot Project to be conducted with Peru will be used to test a number of these ideas within the context of a combined natural disaster event and disease outbreak.
III. Security considerations
No security considerations have been made for this document.
All questions regarding this document should be directed to the editor or the contributors:
|Alan Leidner||NYC Geospatial Information Systems and Mapping Organization (GISMO)||Editor/Contributor|
|Mark Reichardt||Open Geospatial Consortium (OGC)||Editor/Contributor|
|Anna Gage||Harvard T.H. Chan School of Public Health (HSPH)||Contributor|
Health Spatial Data Infrastructure Concept Development Study Engineering Report
1. Terms, definitions and abbreviated terms
This document uses the terms defined in OGC Policy Directive 49, which is based on the ISO/IEC Directives, Part 2, Rules for the structure and drafting of International Standards. In particular, the word “shall” (not “must”) is the verb form used to indicate a requirement to be strictly followed to conform to this document and OGC documents do not use the equivalent phrases in the ISO/IEC Directives, Part 2.
This document also uses terms defined in the OGC Standard for Modular specifications (OGC 08-131r3), also known as the ‘ModSpec’. The definitions of terms such as standard, specification, requirement, and conformance test are provided in the ModSpec.
For the purposes of this document, the following additional terms and definitions apply.
1.1. Terms and definitions
1.1.1. spatial data infrastructure
a comprehensive package of consensus and initiatives required to enable complete provision of data, access and privacy within the territory of the designated infrastructure (source: OGC http://www.opengis.net/def/glossary/term/SDIGDI)
1.2. Abbreviated terms
Air Quality Index
COVID-19 Community Vulnerability Index
Centers for Disease Control (U.S.)
Concept Development Study
Department of Health
Emergency Support Functions
Health Insurance Portability and Accountability Act of 1996 (U.S.)
Health Management Information Systems
Incident Command System
Médecins Sans Frontières (Doctors Without Borders)
National Incident Management System
Personal Health Information
Request For Information
Spatial Data Infrastructure
social vulnerability index
Clause 3 presents the background, justification, and goals for this Concept Development Study (CDS).
Clause 4 summarizes the questions posed in the Request for Information (RFI).
Clause 5 compiles the responses which were received from the RFI and subsequent response validation workshop.
Clause 6 presents the proposed health SDI data Model
Clause 7 covers the study conclusions and recommended next steps
3.1. The importance of data for disaster preparedness and response
Information is an essential part of being able to do almost anything. It is through information that we can pinpoint where things happen, understand what needs to be done, and then support the actual doing. That is why we are now in the “information age” where powerful tools of data creation, organization, analysis and application dominate almost everything that is done in society.
In the realm of disaster preparedness, mobilization, response, and recovery, data that provides situational awareness, a common operating picture, and the inputs into applications, operations, and decisions, has long been understood to be vital to success.
This Health Disaster Concept Development Study (CDS) is based on the certainty that the information associated with a disease outbreak needs to be systematically collected, organized, analyzed, shared, and used effectively in order to make its biggest possible contribution to the support of containment and suppression efforts.
Ever since Dr. Snow’s mapping of Cholera cases in London, spatial (e.g., location) data and analysis has been an essential part of epidemiology.
Figure 1 — Dr. John Snow’s London Cholera Map, mapped to the address point, 1854
Recently (2000), the New York City (NYC) response to West Nile Fever demonstrated the effectiveness of advanced spatial analytics to successfully contain a deadly disease.
Figure 2 — NYC Department of Health and Dr. Sean Ahearn, Hunter College, West Nile Fever predictive model with address points anonymized and aggregated into a grid for analysis.
Disease is a spatial event, and location plays a role in almost every aspect of efforts to successfully deal with it. Infectious diseases spread from person to person and from place to place. Environmental health emergencies are also determined by the location and dispersion of dangerous substances across populated areas.
A health disaster such as a pandemic or epidemic, or a long-term chronic health condition, occurs against a backdrop of location and time. Where people get sick and how and where they spread disease to others is fundamental to understanding how to contain and suppress it. To answer the question “where” requires the support of a spatial data infrastructure (SDI). In high income countries this could be in the form of a robust national, state and local enterprise-wide SDI, including basemaps, foundation layers, and hundreds of functional layers from different agencies and organizations. Low- and middle-income countries (LMIC) also have national spatial data, but it may lack accuracy and comprehensiveness. It is important to understand the gap between health data needed and health data already on hand. It is also necessary to keep in mind that a large-scale health event generates huge amounts of new data that must be integrated with current data holdings. To manage and use this data properly requires a comprehensive data architecture.
The outbreak of a severe disease affects every sector of society, impacting the economy, jobs, education, government services, food supply, and transportation. Every person whether healthy, infected, or recovered will feel its effects. Therefore the spatial response to a major health event will need personal and population information as well as information about many different types of businesses, facilities, and infrastructures. Among the most important features of this information will be location, because only a common location framework will be able to relate all the information inputs to one another. It is combinations of data, drawn together by a common geography, that will make diverse data sets interoperable, allowing applications, models, and other spatial analytic tools to create actionable intelligence that supports public health decision making and operations. In short, the integrative power of conferring spatial identity to almost every aspect of a major health event depends upon getting location right.