Infectious diseases genomic surveillance capacity in the Caribbean: a retrospective analysis of SARS-CoV-2
Bibliographic record
Abstract
Background: The ongoing coronavirus diseases 2019 (COVID-19) pandemic with its numerous variants of concern has shown the need to have a robust and complete global infectious diseases genomic surveillance network worldwide. Various clinical and research institutions have stepped up to perform SARS-CoV-2 sequencing thus enhancing the understanding of this virus' global evolution. However, given that genomic sequencing capacities and capabilities are not available in every region or country, significant gaps exist, which lead to geographic blind spots. One such region is the Caribbean. This paper measures the Caribbean region's SARS-CoV-2 genomic sequencing capacity and highlights the need to improve further regional genomics surveillance capacities and capabilities, which are essential for efficient health interventions for infectious diseases. Methods: A map showing SARS-CoV-2 sequences available for each Caribbean Island was constructed using SARS-CoV-2 genomic, epidemiological and populational data obtained from GISAID, the World Health Organization, the United Nations, and the World Bank. The number of reported SARS-CoV-2 cases and the proportion of cases sequenced in each Caribbean Island was then analysed by the Gross Domestic Product per capita and political status. Findings: As of August 6, 2022, the number of SARS-CoV-2 sequences from the Caribbean are underrepresented with only 40,190 (1.07%) of the over 3.76 million documented cases sequenced, which is further exacerbated by a disparity based not only on the country's income but also on its political status (sovereign country versus dependent or integrated) and accessibility to sequencing technologies. There are a limited number of sequencing centres based in the Caribbean islands with the majority located on the American and European continents. Using mobile sequencing technologies while concomitantly investing in data analysis training could lead to greater and more sustainable coverage. Interpretation: Considering the Caribbean region's dispersed heterogeneous populations, varying political regimes, and resource-constrained healthcare systems, further development of local next-generation sequencing capacity and capabilities in the Caribbean region is needed to achieve global public health goals. Funding: No funding source was required for this study.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".