Geospatial distribution and risk factors of COVID-19 in a low-density municipality in Minas Gerais, Brazil
Bibliographic record
Abstract
Introduction: The human population has faced several pandemics throughout history, with the most recent being COVID-19. Studies on COVID-19 in Brazil, in general, have primarily focused on the country as a whole or on large urban centers. However, prevention measures should also consider smaller municipalities, as the disease has significantly affected these areas as well. Objective: To evaluate the geospatial distribution and risk factors associated with SARS-CoV-2 infection in residents of a low-population-density municipality in the state of Minas Gerais, Brazil. Material and Methods: This retrospective cross-sectional study collected data from COVID-19 notification forms recorded by the Municipal Health Surveillance in Santos Dumont, Minas Gerais, Brazil, from March 2020 to July 2021. Variables associated with SARS-CoV-2 infections were evaluated using explanatory univariate and multivariate logistic regression models. The occurrence of possible spatial clusters among the reported COVID-19 cases in the municipality was assessed using Kernel Density Estimation (KDE) and Spatial Scan analyses. The main variables explored as explanatory for SARS-CoV-2 infections were race/ethnicity, gender, and health-related occupations. Results: Out of 8,271 individuals with suspected COVID-19 in Santos Dumont, 55% (4,595) declared themselves as residents of the municipality. Among these, 4,093 had complete records for spatial analysis, with 1,274 (31%) testing positive for SARS-CoV-2. The choropleth map revealed that infections were concentrated in the central region of the municipality. Univariate analysis showed no statistically significant differences in infection rates based on gender or race/color. However, multivariate analysis indicated that non-health professionals had a significantly higher risk of SARS-CoV-2 infection (OR 2.042; 95% CI 1.41-2.94). Conclusion: The central, denser area of the municipality was more susceptible to SARS-CoV-2 transmission. Additionally, non-health professionals faced higher risks of infection. These findings can serve as tools for the development of public health policies to control future pandemics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".