COVID-Inconfidentes - SARS-CoV-2 seroprevalence in two Brazilian urban areas in the fourth quarter 2020: study protocol and initial results
Bibliographic record
Abstract
Objective: To describe study protocol and initial results of research project COVID-Inconfidentes. Method: This paper described the methodological procedures adopted and the prevalence of the SARS-CoV-2 infection in the population. A household survey was conducted between October and December 2020, in two historic cities of Brazil's mining region. Anti-SARS-CoV-2 antibody was detected using the Wondfo® rapid test. The face-to-face interview consisted of administration of a questionnaire containing registration data, sociodemographic and economic variables, living habits, general health condition, mental health, sleep habits, and eating and nutrition. Results: We evaluated 1,762 residents, of which 764 (43.4 %) were in Mariana and 998 (56.6 %) in Ouro Preto. For both cities, 51.9 % of the interviewees were female, with a predominance of the age range 35 to 59 years old (47.2 %). The prevalence of the SARS-CoV-2 infection was 5.5 % in both cities, 6.2 % in Ouro Preto, and 4.7 % in Mariana (p-value > 0.05). Conclusion: The study was effective to estimate the seroprevalence of infection by the virus and its findings will enable further analyses of the health conditions of the population related to social isolation and the risk of infection with SARS-CoV-2.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".