Genomic epidemiology of the main SARS‐CoV‐2 variants in Italy between summer 2020 and winter 2021
Bibliographic record
Abstract
Abstract Since the beginning of the pandemic, SARS‐CoV‐2 has shown a great genomic variability, resulting in the continuous emergence of new variants that has made their global monitoring and study a priority. This work aimed to study the genomic heterogeneity, the temporal origin, the rate of viral evolution and the population dynamics of the main circulating variants (20E.EU1, Alpha and Delta) in Italy, in August 2020–January 2022 period. For phylogenetic analyses, three datasets were set up, each for a different main lineage/variant circulating in Italy in that time including other Italian and International sequences of the same lineage/variant, available in GISAID sampled in the same times. The international dataset showed 26 (23% Italians, 23% singleton, 54% mixed), 40 (60% mixed, 37.5% Italians, 1 singleton) and 42 (85.7% mixed, 9.5% singleton, 4.8% Italians) clusters with at least one Italian sequence, in 20E.EU1 clade, Alpha and Delta variants, respectively. The estimation of tMRCAs in the Italian clusters (including >70% of genomes from Italy) showed that in all the lineage/variant, the earliest clusters were the largest in size and the most persistent in time and frequently mixed. Isolates from the major Italian Islands tended to segregate in clusters more frequently than those from other part of Italy. The study of infection dynamics showed a positive correlation between the trend in the effective number of infections estimated by BSP model and the Re curves estimated by birth‐death skyline plot. The present work highlighted different evolutionary dynamics of studied lineages with high concordance between epidemiological parameters estimation and phylodynamic trends suggesting that the mechanism of replacement of the SARS‐CoV‐2 variants must be related to a complex of factors involving the transmissibility, as well as the implementation of control measures, and the level of cross‐immunization within the population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".