Spatial and Temporal Hotspot Analysis of COVID-19 in Toronto
Bibliographic record
Abstract
ABSTRACT The COVID-19 pandemic in Toronto, Canada was unequal for its 2.7 million residents. As a dynamic pandemic, COVID-19 trends might have also varied over space and time. We conducted a spatiotemporal hotspot analysis of COVID-19 over the first four major waves of COVID-19 using three different applications of Moran’s I to highlight the variable experience of COVID-19 infections in Toronto, while describing the potential impact of socioeconomic and sociodemographic factors on increased risk of COVID-19 exposure and infection. Results highlight potential clustering of COVID-19 case rate hot spots in areas with higher concentrations of immigrant and low-income residents and cold spots in areas with more affluent and non-immigrant residents during the first three waves. By the fourth wave, case rate clustering patterns were more dynamic. In all, a better understanding of the unequal COVID-19 pandemic experience in Toronto needs to also consider the dynamic nature of the pandemic. HIGHLIGHTS - The COVID-19 pandemic was spatially and temporarily dynamic in the City of Toronto. - At first, hotspots were concentrated in areas with more marginalized residents. - Later, COVID-19 spatial trends diverged from initially identified patterns. - East Asian enclaves in the city disproportionally had lower COVID-19 case counts. - COVID-19 studies need to consider the dynamic nature of the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".