Analyzing the Relationship Between COVID-19 and Sociodemographic and Environmental Factors: A Case Study in Toronto
Bibliographic record
Abstract
COVID-19 has disproportionately impacted communities based on sociodemographic and environmental factors. Previous studies have largely focused on traditional statistical models to investigate these disparities with limited attention to within-city variations. This research addresses this gap by employing advanced machine learning models to predict COVID-19 case counts at the neighborhood level within Toronto. Using algorithms such as Support Vector Regression, Random Forest, Gradient Boosting, and XGBoost, along with SHAP (SHapley Additive exPlanations) analysis, we identify key factors impacting COVID-19 transmission, including air pollution, socioeconomic status, and racialized group membership. Our results demonstrate that sociodemographic factors significantly influence sporadic cases, while environmental factors, particularly air pollutants, are critical in outbreak cases. This study highlights the value of machine learning in understanding complex interactions between risk factors with implications for targeted public health interventions to mitigate COVID-19 disparities.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".