Interoperability of COVID-19 Clinical Phenotype Data with Host and Viral Genetics Data

Bernasconi, Anna; Ceri, Stefano

doi:10.3390/biomed2010007

Cited by 4 publications

(3 citation statements)

References 75 publications

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“…Tools such as SnpEff [79], CorGAT [80], or the Coronavirus Typing Tool [81] may be used for annotating the viral information, within protected environments such as VirusLab [82]. A correct ontological characterization of concepts is necessary for 1) interpreting all the aspects related to the production of virus sequence, 2) establishing a correct host-virus link, and 3) supporting effective interoperability [17], thereby enabling the design of models that correlate clinical outcomes with the characteristics of the virus.…”

Section: Discussionmentioning

confidence: 99%

“…The VCM focuses on such structuring function for the purposes of characterizing viral genomic sequences. The model has already being successfully used for designing several search and visualization systems [14][15][16] and has been linked to a Phenotype Data Dictionary [17] and a knowledge base of SARS-CoV-2 mutations' impacts [18,19]. A broader application and adoption of the VCM within the context of virology research (by both life science domain experts and information systems' developers), however, requires that a more ontology-oriented approach is embraced, allowing the unambiguous identification of entities in the context of heterogeneous information.…”

Section: Introductionmentioning

confidence: 99%

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Semantic interoperability: ontological unpacking of a viral conceptual model

2022

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Background Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers. Results In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the “ontological unpacking” method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it. Conclusions We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the “I” in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantic interoperability: ontological unpacking of a viral conceptual model

2022

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“…In addition, the platform has created new opportunities to monitor population health status and generate health intelligence in future emergencies and apply novel analytic techniques. Our initiatives complement similar efforts in other jurisdictions ( 4 , 26 ), and together highlight their potential in jurisdictions without existing platforms. While implementation elsewhere may be limited by local availability of datasets, the overall concept of the platform could be considered broadly feasible given available resources, facilitating regulations, and successful stakeholder collaboration.…”

Section: Discussionmentioning

confidence: 61%

Cohort profile: the British Columbia COVID-19 Cohort (BCC19C)—a dynamic, linked population-based cohort

Wilton,

Abdulmenan,

Chong

et al. 2024

Front. Public Health

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PurposeThe British Columbia COVID-19 Cohort (BCC19C) was developed from an innovative, dynamic surveillance platform and is accessed/analyzed through a cloud-based environment. The platform integrates recently developed provincial COVID-19 datasets (refreshed daily) with existing administrative holdings and provincial registries (refreshed weekly/monthly). The platform/cohort were established to inform the COVID-19 response in near “real-time” and to answer more in-depth epidemiologic questions.ParticipantsThe surveillance platform facilitates the creation of large, up-to-date analytic cohorts of people accessing COVID-19 related services and their linked medical histories. The program of work focused on creating/analyzing these cohorts is referred to as the BCC19C. The administrative/registry datasets integrated within the platform are not specific to COVID-19 and allow for selection of “control” individuals who have not accessed COVID-19 services.Findings to dateThe platform has vastly broadened the range of COVID-19 analyses possible, and outputs from BCC19C analyses have been used to create dashboards, support routine reporting and contribute to the peer-reviewed literature. Published manuscripts (total of 15 as of July, 2023) have appeared in high-profile publications, generated significant media attention and informed policy and programming. In this paper, we conducted an analysis to identify sociodemographic and health characteristics associated with receiving SARS-CoV-2 laboratory testing, testing positive, and being fully vaccinated. Other published analyses have compared the relative clinical severity of different variants of concern; quantified the high “real-world” effectiveness of vaccines in addition to the higher risk of myocarditis among younger males following a 2nd dose of an mRNA vaccine; developed and validated an algorithm for identifying long-COVID patients in administrative data; identified a higher rate of diabetes and healthcare utilization among people with long-COVID; and measured the impact of the pandemic on mental health, among other analyses.Future plansWhile the global COVID-19 health emergency has ended, our program of work remains robust. We plan to integrate additional datasets into the surveillance platform to further improve and expand covariate measurement and scope of analyses. Our analyses continue to focus on retrospective studies of various aspects of the COVID-19 pandemic, as well as prospective assessment of post-acute COVID-19 conditions and other impacts of the pandemic.

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Conceptual Modeling for Bioinformatics

Bernasconi,

García S.

2024

Reference Module in Life Sciences

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Interoperability of COVID-19 Clinical Phenotype Data with Host and Viral Genetics Data

Cited by 4 publications

References 75 publications

Semantic interoperability: ontological unpacking of a viral conceptual model

Semantic interoperability: ontological unpacking of a viral conceptual model

Cohort profile: the British Columbia COVID-19 Cohort (BCC19C)—a dynamic, linked population-based cohort

Conceptual Modeling for Bioinformatics

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