Investigating the Relationships Between COVID-19 Cases, Public Health Interventions, Vaccine Coverage, and Mean Temperature in Ontario and Toronto
Bibliographic record
Abstract
Background/Objectives: We aimed to examine the relationship between COVID-19 cases and Public Health Interventions (PHIs), vaccine coverage, and temperature. We compared our findings with those of other studies that used different methodologies, such as mathematical models. Methods: We developed monthly PHI scores using the Oxford COVID-19 Government Response Tracker from May 2020 to May 2021. We calculated PHI scores by summing the highest monthly score of each intervention and expressed the PHI score as a percentage of the maximum. We obtained vaccine coverage and temperature data from January 2021 to September 2023. We calculated Spearman’s rank-order correlation coefficients to examine correlations. Results: The correlation between cases and PHI was positive (ρ = 0.947, p < 0.0001). The correlation between cases and vaccine coverage was approximately zero (ρ = 0.0165, p = 0.957) from January 2021 to January 2022 and was negative from February 2022 to September 2023 (ρ= −0.816, p < 0.0001). The correlation for cases and temperature was negative from January 2021 to January 2022 (ρ = −0.676, p = 0.0112) and was almost zero from February 2022 to September 2023 (ρ = −0.162, p = 0.494). The models showed a negative correlation between PHI and vaccine coverage, and mixed results for temperature. Conclusions: There was a positive correlation between cases and PHI. Prior to reaching the vaccine threshold coverage, there was no correlation for vaccination and a negative correlation for temperature. Post-vaccine threshold, there was a negative correlation for vaccination and no correlation for temperature. Correlation results for PHI and temperature differed from those of the mathematical models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".