Seroprevalence of SARS-CoV-2 before/after case zero
Bibliographic record
Abstract
Abstract Introduction Italy was one of the first EU countries hit by the COVID-19 pandemic. Currently, Italy has reported 15.5 million cases of COVID-19 and 161000 deaths. Meanwhile, the vaccination campaign against COVID-19 began in Italy at the end of 2020, using mRNA and viral vector vaccines (immunizing people against Spike protein of SARS-CoV-2. The purpose of this study was to estimate, in a representative sample of the Italian population, the prevalence of antibodies against SARS-CoV2 in 2019 (before case zero, identified in Italy in February 2020) and in 2021, after 3 pandemic waves and a vaccination campaign. Methods During October / November 2019: 365 participants were selected in the Piedmontese population among those who went to a hospital for routine blood tests. The population was selected on the basis of age and gender to be representative of the Italian population. The same number of patients was selected in the first quarter of 2021, the inclusion and exclusion criteria remained the same. Sera were searched for spike protein of SARS-CoV-2 and, if positive, tested for anti-nucleocapsid antibodies. Results Our preliminary data show that half of the sample for both years is female. In the 2019 sample, i.e. before case zero was identified in Italy (Lombardy), five of the sera (4 males and one female) tested positive for anti-Spike,indicating a previous infection (vaccine didn't exist). In the 2021 sample, 152 males and 139 females tested positive for IgG anti-spike, for a total of 291. The prevalence therefore passed from 1.37% to 79.73%. As regards the search for ANti-Nantibodies, one male and one female tested positive in 2019; in 2021 9 males and 13 females. Conclusions The results of our study show that in 2019, before the first official case in Italy was highlighted, coronavirus was already circulating. The prevalence has risen exponentially, going from less than 2% to around 80%. Key messages • Covid-19 was circulating in Italy in 2019. • Seroprevalence of anti-S in 2021 was about 20%.
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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 | 0.001 | 0.000 |
| 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".