ISO/TS 8376:2023

Technical
Specification
ISO/TS 8376
First edition
Genomics informatics —
2023-12
Requirements for interoperable
systems for genomic surveillance
Reference number
© ISO 2023
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Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Abbreviated terms . 3
5 Design principles . 4
5.1 Overview .4
5.2 Explicitness .4
5.3 Scalability .4
5.4 Transparency .5
5.5 Extensibility .5
5.6 Trust and cooperation . .5
6 Service and standards requirements and recommendations . 6
6.1 Overview .6
6.2 Data representation .6
6.2.1 Data identifiers .6
6.2.2 Platform/vendor agnostic data access and retrieval .6
6.2.3 Standard data models and formats.6
6.3 Data discovery .6
6.3.1 Web interfaces for data search and discovery .6
6.3.2 Discoverability and networking web service .7
6.4 Data access .7
6.4.1 Researcher authorization .7
6.4.2 Data access decision making .7
6.5 Data analysis .8
6.5.1 Web interfaces for workflow execution and monitoring .8
6.5.2 Registration and sharing of computational tools.8
6.5.3 Languages for writing reproducible workflows .8
7 Data linkage . 8
7.1 Overview .8
7.2 Genomic and other ‘omics .9
7.3 Epidemiology . .9
7.4 Medical records .9
Annex A (informative) Examples of federated networks . 10
Bibliography .12

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Foreword
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This document was prepared by Technical Committee ISO/TC215, Health informatics, Subcommittee SC 1,
Genomics Informatics.
Any feedback or questions on this document should be directed to the user’s national standards body. A
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Introduction
0.1 Rationale
In a world where international travel and trade is essential, a pathogen affecting one region or country
can rapidly spread around the globe. One of the most important tools in responding to infectious disease
is genomic surveillance, the process of constantly monitoring pathogens, and analysing their genetic
similarities and differences.
Genomic surveillance is transforming public health action by providing a deeper understanding of
pathogens, their evolution and proliferation. Alongside clinical, epidemiological and other multi-source
data, genomic data for potentially dangerous pathogens informs risk assessments, enables governments or
non-governmental organizations (NGOs) to track emerging or spreading infectious diseases, and supports
tailored recommendations for prevention. For example, governments can use genomic surveillance to guide
policy or public health measures, academic and research organizations can interrogate the pathogen and
understand its impact, and individuals can be better informed about potential risks.
To make use of pathogen genomics data, it must be interpreted using contextual data, such as sample
metadata, laboratory methods, patient demographics, clinical outcomes, and epidemiological information,
underscoring the importance of incorporating a variety of data in surveillance tools. Due to the importance
of contextual data in the interpretation of pathogen data collected by a network of independent data
acquisition nodes, when this document refers to “data”, it means both genomic and contextual data. Similarly,
all derivatives such as “data access” and “data sharing” include contextual data as well.
The focus of this document is genomic surveillance of pathogens; however, it is important to consider the
role of multi-source data when building federated surveillance systems, including health records and
administrative data, in understanding the clinical significance of a given pathogenic variant or prevention
strategies. Scientists are also increasingly looking at the environment, including human host genetics, to
identify biomarkers that can explain susceptibility to as well as severity of disease.
The emergence of SARS-CoV-2 and impact of the COVID-19 pandemic crystalized the need for coherent
regional, national and global genomic surveillance systems. The world needs timely, high quality and
geographically representative data in as close to real-time as possible. To realize the benefits of genomic
surveillance data needs to be shared across jurisdictions, both within and between countries, through
networks, systems and platforms.
Sharing pathogen genome data is critical for preventing, detecting and responding to epidemics at national
and international level, as well as monitoring and responding to endemic diseases and tracking antimicrobial
resistance. However, genomic surveillance presents challenges, in terms of the infrastructure, capacities
and capabilities needed, and the harmonization across systems and countries to be able to compare and use
the data effectively. Digital systems for genomic surveillance are becoming increasingly available, however,
they are not being built on common design principles and rarely use standards that enable them to interact
as nodes in a national interoperable digital network and even less so in a highly dynamic international
ecosystem.
The generation of high-quality pathogen genomic data that can be shared quickly and effectively in a global
system requires capacity and infrastructure. Building upon current and new advances in genomic data
sharing digital systems and platforms and to enable future effective, quality, safe, understood, timely and
accurate genomics data sharing and surveillance, a common set of design principles, services requirements
and standards must be rapidly determined, quickly adopted through consensus and widely published to
realize such large-scale interoperability. As countries and organizations begin to build a stronger global
architecture for health emergency preparedness and response, global standards are critical to support
interoperability and collaboration, particularly in a federated model.

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0.2 Importance of sharing data and benefits to regional, national, and international response
The COVID-19 pandemic underscores the importance of interoperable solutions to facilitate rapid data
sharing and data governance to support critical pandemic response activities. In a globalized world,
successful pandemic response depends on nation states and regions rapidly communicating accurate
information, including pathogen identification, incidence, transmission patterns and mortality to the
international community. This information allows regional and national jurisdictions, as well as international
organizations, to implement targeted and comprehensive control measures as quickly as possible, protecting
at the utmost the health and safety of the citizens or populations they serve.
The importance of sharing data to address global health priorities, including informing responses to
outbreaks and epidemics, is now widely recognized. In the context of COVID-19, widespread mandates for
data sharing were established by several international stakeholders including national governments, global
health NGOs, scientific journals, research funders, and research institutions. In epidemics and pandemics,
the case for such practices is especially urgent and required to develop much-needed vaccines, therapeutics
and diagnostics.
For example, researchers in China sequenced the SARS-CoV-2 genome — the virus that causes COVID-19 in
humans — and made the data publicly available through an open access platform in January 2020, which
sped up the development of critical diagnostic assays. As the virus spread and mutated, becoming more
virulent and more transmissible, data from around the world enabled countries to rapidly change public
policy and enabled vaccine development at an unprecedented pace or scale.
Further, widespread public and private data sharing across domains and borders during the COVID-19
pandemic generated insights never seen before at this scale. Through linkages between viral genomic and
other types of data (such as policy or mobility data), public health bodies and decision makers could model
the potential impact of border or workplace closures.
0.3 Level of interoperability
This document is intended to highlight the requirements for interoperability between networks, systems
and platforms required to share data within and between organizations and countries. It focuses on the
technical, structural and syntactic levels of interoperability, while acknowledging the importance of semantic
interoperability. It does not however address business, organization, or other types of interoperability
that aim to address differences in organizational perspectives within the genomic surveillance domain.
It does not describe methods or best practices for data management, which might need to be harmonized
to make use of data shared within systems. The problem spaces, use cases and data access patterns in
which genomic data plays a key role also continue to grow, resulting in a need to build an accompanying
framework of extensible biomedical workflow profiles, which would then provide the specific contexts of
system interoperability. Such frameworks and profiles are not the topic of this document.
Additionally, this document does not model, represent or manage genomic surveillance as a system,
framework, or domain; nor does it provide a data model or information model for genomic surveillance; nor
is it intended as a generic system architecture for genomic surveillance knowledge level interoperability. It
is understood such generic and formal genomic surveillance modelling work is seen as a potential valuable
and useful standards activity and would be greatly aided and founded in the formal system-oriented,
architecture-centric, ontology-based and policy-driven approach as standardized in ISO 23903. Modelling
genomic surveillance for multi-domain interoperability requires the advancement from data models,
information models and Information Communication Technology (ICT) domain knowledge perspectives to
the knowledge perspective of genomic surveillance with an abstract, domain-independent representation
for genomic surveillance systems. Such work can generate an ISO 23903 interoperability and integration
reference architecture instance. That said, the model and framework for formally modelling and managing
genomic surveillance and its behaviour and creating an interoperability and integration reference
architecture instance is beyond the scope of this document. It is recognized and acknowledged that
should such genomic surveillance standard modelling processes and the associated, explicit, formalized
ISO 23903-based integration and reference architecture instance for genomic surveillance knowledge level
interoperability and harmonization be an agreed, completed and published ISO specification, it may require
adaptation or revision of this document.

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0.4 Data federation
Data federation is a technique that allows search and data analysis to be performed across multiple
distributed datasets, with each individual dataset remaining in its protected local environment, instead
of copying or moving data into a single centralized location. The centralized and federated models of data
sharing are described in Figure 1.
Key
A centralization
B federation
1 Data from multiple sources are moved into a central location to be queried. Data custodians relinquish control over
the data and cannot directly enforce access policies.
2 Data from multiple sources are queried through a system, network, or platform that facilitates access, enabling each
data custodian to maintain control of the data.
Figure 1 — Overview of the centralized and federated models of data sharing
Since the location of the data does not change, the data custodian responsible for the dataset maintains
administrative control of the data, including privacy, security and access based on consent. Further, data
generators have transparency into how the data are used and can enforce attribution policies. In a federated
system, researchers send their questions to the data and do not have direct access to the data. Instead of
creating and distributing multiple copies of large files to researchers looking to analyse the data, a single
copy of sensitive data is created and stored in the same region as the data was generated. Through a
federated system, network, or platform, distributed data can be linked to other relevant data, for example
connecting genomic data with clinical or administrative data.
Data federation is particularly valuable in genomic surveillance, where a large volume or diversity of data
is required to generate insights, and where real time data and regional representation benefit the global
community and regional response. However, data federation requires navigating different data policy laws,
security and privacy protocols, and data interoperability challenges.

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0.5 Technological foundation for secure data sharing
The need to share data between and within organizations in the healthcare and life sciences sector has long
been recognized, however broad sharing of data has been limited due to privacy and security considerations,
and interoperability across systems.
The necessary technologies to address interoperability are being developed and implemented in
the healthcare and life sciences sector of cloud computing, application programming interface (API)
1)
management, cybersecurity, as well as access to lightweight health resources, such those defined by HL7®
2)
FHIR® . A central component of federated data systems is the use of APIs and foundational architecture,
which enables a scalable, secure and reliable means of accessing data from data custodians, particularly as
data sources likely use different underlying technologies and data formats.
Various solutions have been developed and most, if not all of them, have used the paradigm of data “exchange”
and even led to creating an entire segment in digital health called electronic Health Information Exchange
(HIE). In such an exchange, data practically changes hands and is transferred from a “data provider” or
“data custodian” to a “data consumer”. With all the benefits and simplicity of exchanging data, it also poses
challenges — duplicating large amounts of data on both sides of the exchange, keeping the data in sync,
keeping track of all transformations that data goes through, enforcing rules on transitive downstream
data exchange, as well as passing the control of contextual data that might contain identifiable personal
information with all the privacy, security and data governance concerns associated with it.
The technological developments in the recent years have enabled us to begin to talk more about “data
sharing” and “data access”, which is substantially different from exchanging copies of data. One sharing data
approach uses a centralized system acting as a broker or intermediary in the form of a “data union” or a “data
cooperative” where data governance rules and “data use contracts” can be defined and strictly imposed.
Another one is via using a truly distributed, federated approach, like the InterPlanetary File System (IPFS)
and blockchain.
Blockchain has enabled a promise of building a new Internet (often referred to as Web3) where digital
assets can not only be read, written and accessed but also be owned, thus giving their “owners” the
exclusive decision of how to share their data and extract value from it. This is extremely important for the
contextual data discussed before, as in some cases it might contain very sensitive information. Blockchain
has also enabled the tracking of the many transformations data goes through and allows the reproduction of
analytical results with confidence guaranteed by data’s cryptographic immutability.
Another foundational technology that enables true federation in an open network is decentralized identifiers
(DID), self-sovereign identity (SSI), verified credentials (VCs) and Trust over IP (ToIP). These technologies
enable dynamically adding trusted nodes to a network as well as uniquely identify datasets and their
derivatives.
0.6 Use cases
Pathogens such as viruses and bacteria are constantly evolving in response to selective pressures, and
these changes result in different characteristics, such as a pathogen being more or less transmissible,
detectable and deleterious. Once a pathogen is identified in the human population, ongoing sequencing and
genomic surveillance facilitates tracking geographic distribution and spread, as well as monitoring genomic
alterations that change characteristics of the pathogen and its impact on the host.
Genomic surveillance tools can be developed or used by countries or governments, non-governmental
organizations (NGOs) or global health initiatives, or industry providing solutions or whose business is
impacted by infectious disease, as well as individuals conducting research in an academic setting. Further,
data or insights facilitated by such tools can be consumed by individuals through mainstream media or
research to understand the personal impact.
1) HL7 is the registered trademark of Health Level Seven International. This information is given for the convenience of
users of this document and does not constitute an endorsement by ISO of the product named.
2) FHIR is a trademark of HL7®. This information is given for the convenience of users of this document and does not
constitute an endorsement by ISO of the product named.

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A generic use case for a genomics surveillance tool could include a public health agency and their data
scientists and epidemiologists, with genome sequence data sources from multiple local or provincial (state)
jurisdictions and multiple additional county ministries of health. Such a use case would demonstrate a
federated surveillance system, built to compare data from local or provincial (state) jurisdictions to open
data from all other jurisdictions in the country, and other jurisdictions worldwide. While a generic use case
is not included in this document, Annex A provides actual in use project examples of federated networks to
support genomic surveillance as well as human genomic research.
It is important to note that given the nature of pathogen surveillance, data collection is ongoing, and both
data and insights are constantly changing. While one of the benefits of data federation is that data can be
shared in near real-time, this also means that the results of a query are reflective of that point in time —
the same query might return different results at different times. Further, in order to generate accurate
insights, data must be transformed and harmonized in such a way that it can be analysed alongside other
data, however this document focuses on interoperability for data sharing at the systems level and does not
address the requirements for data.

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Technical Specification ISO/TS 8376:2023(en)
Genomics informatics — Requirements for interoperable
systems for genomic surveillance
1 Scope
This document outlines the design principles and the service and standards requirements to enable an
interoperable system for genomic surveillance (herein referred to as “federated surveillance system”),
including data representation, discovery and analysis, and data linkage.
Using select profiles this document applies to genomics digital systems, networks and platforms that enable
a federated approach for researchers, clinicians, and patients in both the private and public sector at the
local, regional, and international levels.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
genomic surveillance
pathogen surveillance
sequencing of genetic material of pathogens to identify and monitor genetic changes linked to the origins or
characteristics of a disease afflicting different people
3.2
interoperability
ability of a system or a product to work with other systems or products without special effort on the part of
the customer
Note 1 to entry: Under traditional ICT focus, interoperability is the ability of two or more systems or components to
exchange information and to use the information that has been exchanged.
[SOURCE: ISO/IEC 2382: 2015, 2120585]
3.3
federated system
federated network
federated database
collection of independent but co-operating database systems that are distributed, autonomous and
heterogeneous
Note 1 to entry: in a federated network, “shared” data are not moved into a centralized location for analysis, but rather
queries are distributed across data sources
[SOURCE: ISO 19297-1:2019, 3.2, modified — “Independent” and “distributed” were added to the definition.]

3.4
data
representation of information in a formal manner suitable for communication, interpretation, or processing
by human beings or computers
[SOURCE: ISO 10303-1:2021, 3.1.29]
3.5
data custodian
custodian
person or entity that has custody, control or possession of electronically stored information and is
responsible for the safe implementation of data access policies
Note 1 to entry: in a federated model, there is only one copy of the data and thus only one data custodian who is
responsible for adjudicating and granting access.
[SOURCE: ISO/IEC 27050-1:2019, 3.2, modified — Access policies were added to the definition and to Note 1.]
3.6
data provider
organization which produces data or reference metadata
[SOURCE: ISO 17369:2013, 2.1.18]
3.7
data consumer
individual or organization that uses data as a starting point
Note 1 to entry: In the research domain, a data consumer is a scientist or research group for commercial or non-
commercial purposes.
Note 2 to entry: In the medical domain, a data consumer can be a physician or patient. In some cases, consumer can
also be payer for commercial or non-commercial purposes.
Note 3 to entry: A data consumer may be any entity that uses data as an input in any form at any time.
[SOURCE: ISO/TR 3985:2021, 3.4]
3.8
data sharing
access to or processing of the same data by more than one authorized entity. In a federated system, sharing
denotes access to the data to be viewed, queried, or analysed without making copies
[SOURCE: ISO/IEC TS 38505-3:2021, 3.7, modified —Additional context included in the federated model.]
3.9
data access
process by which a user or a system can retrieve or read published data on another system
Note 1 to entry: This data access happens over a network connection and the data typically does not persist after the
connection is terminated.
[SOURCE: ISO 5127:2017, 3.1.11.17, modified — Note to entry has been added (taken from ISO/IEC 22624:2020,
3.5).
...