Unveiling the Dynamics of the Omicron Variant: Prevalence, Risk Factors, and Vaccination Efficacy during the Third Wave of Covid-19 in Indonesia's Gowa Regency
Bibliographic record
Abstract
Introduction: In February-March 2022, the B.1.1.529 (Omicron) variant of SARS-CoV-2 became the cause of the third wave of COVID-19 in Indonesia. However, data on the prevalence of the effects of the third wave of the COVID-19 pandemic are still limited, especially in regencies/cities in Indonesia. Gowa Regency is one of the most affected areas by COVID-19 in South Sulawesi. Objective: Ascertaining risk factors associated with infection and evaluating the effectiveness of vaccination programs in Gowa Regency. Methods: In March 2022, venous blood specimens were taken from 859 randomly selected samples in Gowa Regency to determine the presence of antibodies to SARS-CoV-2 by examining chemiluminescent microparticle immunoassay (CMIA) specimens. Information on demographics, previous infection history, symptoms, comorbid diseases, and vacancy status was collected through interviews. Data analysis was conducted using descriptive, bivariate tests with chi-square and One-way ANOVA, and multivariate tests using logistic regression. Results: The overall prevalence of anti-SARS-CoV-2-IgG was 98.7%. The results showed that the prevalence of SARS-CoV-2 antibodies was not significantly different in terms of sex (P=0.306), age group (P=0.190), education (P=0.749), and occupation (P=0.685), history of COVID-19 symptoms (P=0.108), history of confirmation of COVID-19 (P=0.352), and history of comorbid diseases (P=0.477). However, this study showed that the prevalence of SARS-CoV-2 antibodies differed significantly among the fully vaccinated and incomplete groups (P <0.001). Conclusion: There was a significant difference between the antibody status of respondents who had been fully vaccinated (at least two doses) and respondents who had not completed the vaccination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".