SARS-CoV-2 introductions to the island of Ireland
Bibliographic record
Abstract
Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had an unprecedented impact on the people of Ireland as waves of infection spread across the island during the global coronavirus disease 2019 (COVID-19) pandemic. Viral whole-genome sequencing (WGS) has provided insights into SARS-CoV-2 molecular mechanisms of pathogenicity and evolution and contributed to the development of anti-virals and vaccines. High levels of WGS have enabled effective SARS-CoV-2 genomic surveillance on the island of Ireland, leading to the generation of a sizeable data set with potential to provide additional insights into viral epidemiology. Because Ireland is an island, accurate documentation of travel rates to and from other regions, both by air and by sea, are available. Furthermore, the two distinct political jurisdictions on the island allow comparison of the impact of varying public health responses on viral dynamics, including SARS-CoV-2 introduction events. Using phylogenomic analysis incorporating sample collection date and location metadata, we describe multiple introduction and spreading events for all major viral lineages to the island of Ireland during the period studied (March 2020–June 2022). The majority of SARS-CoV-2 introductions originated from England, with frequent introductions from USA and northwestern Europe. The clusters of sequences predicted to derive from discrete introduction events (“introduction clusters”) vary greatly in size, with some involving only one or two cases and others comprising thousands of samples. When introduction cluster samples are mapped sequentially by collection date, they appear predominantly in previously affected or adjacent areas. This mirroring of the phylogenetic relationships by the geospatiotemporal propagation of SARS-CoV-2 validates our analytic approach. By downsampling, we estimate the power to detect introductions to Ireland as a function of sequencing levels. Per capita normalisation of both sequencing levels and detected introductions accounts for biases due to differing sequencing efforts and total populations. This approach showed similar rates of introductions for all major lineages into Northern Ireland (NI) and Republic of Ireland (RoI) with the exception of Delta, which was higher in NI which is likely attributable to higher travel per capita. However, there were similar rates of Delta infection within NI and RoI, suggesting that although travel restrictions will reduce risk of introducing novel variants to the region, they may not substantially decrease total incidence. Our generalisable methodology to study introduction dynamics and optimal sequencing levels will assist public health authorities to select the most appropriate control measures and viral sequencing strategy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".