SARS-CoV-2 genomic contextual data harmonization: recommendations from a mixed methods analysis of COVID-19 case report forms across Canada
Bibliographic record
Abstract
BACKGROUND: The timely sharing of public health information is critical during a pandemic and is an obstacle that Canada has yet to fully address. During the COVID-19 pandemic, sequencing of the SARS-CoV-2 genome enhanced our understanding of transmission patterns, aided in identifying variants of concern, and supported the development and evaluation of diagnostic tests and vaccines. The Canadian national response faced challenges in aggregating genomic contextual data and carrying out integrated analysis across regions partly due to disparities in COVID-19 case report forms used to capture epidemiological and clinical data that accompanies SARS-CoV-2 sequence data. Such variations delay data integration and make consistent analysis difficult or impossible. The objective of this work was to understand what information was being collected from COVID-19 case report forms used across Canada and identify potential contextual data harmonization issues and solutions. METHODS: Provincial/territorial/national Canadian COVID-19 case report forms were subjected to field-by-field comparisons to identify variations in data categorization, structures, formats, types, granularity, ambiguity, and questions asked. Federal epidemiologists were consulted to substantiate the results. RESULTS: Data harmonization issues and common data elements were identified. We make recommendations for better national coordination, integrated databases, and data harmonization tools. CONCLUSION: This report compares data elements of the various case report forms used across Canada to identify overlaps and differences in the collection method of COVID-19 case information, while also highlighting data harmonization complications and potential solutions. Identifying available data elements will better guide COVID-19 surveillance and research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.424 | 0.555 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.024 | 0.030 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.014 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".