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.

IV.  Submitters

All questions regarding this document should be directed to the editor or the contributors:

Name	Organization	Role
Alan Leidner	NYC Geospatial Information Systems and Mapping Organization (GISMO)	Editor/Contributor
Mark Reichardt	Open Geospatial Consortium (OGC)	Editor/Contributor
Josh Lieberman	OGC	Editor/Contributor
Anna Gage	Harvard T.H. Chan School of Public Health (HSPH)	Contributor