Corona Immunitas: study protocol of a nationwide program of SARS-CoV-2 seroprevalence and seroepidemiologic studies in Switzerland
Bibliographic record
Abstract
OBJECTIVES: Seroprevalence studies to assess the spread of SARS-CoV-2 infection in the general population and subgroups are key for evaluating mitigation and vaccination policies and for understanding the spread of the disease both on the national level and for comparison with the international community. METHODS: Corona Immunitas is a research program of coordinated, population-based, seroprevalence studies implemented by Swiss School of Public Health (SSPH+). Over 28,340 participants, randomly selected and age-stratified, with some regional specificities will be included. Additional studies in vulnerable and highly exposed subpopulations are also planned. The studies will assess population immunological status during the pandemic. RESULTS: Phase one (first wave of pandemic) estimates from Geneva showed a steady increase in seroprevalence up to 10.8% (95% CI 8.2-13.9, n = 775) by May 9, 2020. Since June, Zurich, Lausanne, Basel City/Land, Ticino, and Fribourg recruited a total of 5973 participants for phase two thus far. CONCLUSIONS: Corona Immunitas will generate reliable, comparable, and high-quality serological and epidemiological data with extensive coverage of Switzerland and of several subpopulations, informing health policies and decision making in both economic and societal sectors. ISRCTN Registry: https://www.isrctn.com/ISRCTN18181860 .
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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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.020 |
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".