Genomic Snapshot of SARS-CoV-2 in Migrants Entering Through Mediterranean Sea Routes
Bibliographic record
Abstract
In December 2019, a novel coronavirus emerged in Wuhan, China, rapidly spreading into a global pandemic. Italy was the first European country to experience SARS-CoV-2 epidemic, and one of the most severely affected during the first wave of diffusion. In contrast to the general restriction of people movements in Europe, the number of migrants arriving at Italian borders via the Mediterranean Sea route in the summer of 2020 had increased dramatically, representing a possible, uncontrolled source for the introduction of novel SARS-CoV-2 variants. Importantly, most of the migrants came from African countries showing limited SARS-CoV-2 epidemiological surveillance. In this study, we characterized the SARS-CoV-2 genome isolated from an asymptomatic migrant arrived in Sardinia via the Mediterranean route in September 2020, in comparison with SARS-CoV-2 isolates arrived in Sicily through the Libyan migration route; with SARS-CoV-2 isolates circulating in Sardinia during 2020; and with viral genomes reported in African countries during the same summer. Results showed that our sequence is not phylogenetically related to isolates from migrants arriving in Sicily, nor to isolates circulating in Sardinia territory, having greater similarity to SARS-CoV-2 genomes reported in countries known for being sites of migrant embarkation to Italy. This is in line with the hypothesis that most SARS-CoV-2 infections among migrants have been acquired prior to embarking to Italy, possibly during the travel to or the stay in crowded Libyan immigrant camps. Overall, these observations underline the importance of dedicated SARS-CoV-2 surveillance of migrants arriving in Italy and in Europe through the Mediterranean routes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".