Shifting patterns of emergency incidents during the COVID-19 pandemic in the City of Vaughan, Canada
Bibliographic record
Abstract
Purpose The COVID-19 pandemic has changed many facets of urban life and operations, including emergency incidents. This study examines how COVID-19 has brought about changes in, and shifting patterns of, emergency incidents in the City of Vaughan, Ontario, Canada. This study aims to derive insights that could potentially inform planning and decision-making of fire and rescue service operations as further stages of the pandemic unfold. Design/methodology/approach Standard temporal analysis methods are applied to investigate the changes in the number and nature of emergency incidents, as recorded sequentially in the city's fire and rescue service incident report database, through various phases or waves of the pandemic and the associated public health measures that have been introduced. Findings The study analyses show a decrease in the number of emergency calls compared to previous reference years. Vehicle-related incidents show the highest decline, and changes in daily and hourly pattens are consistent with public health measures in place during each stage of the pandemic. The study concludes that the COVID-19 pandemic has had a significant impact on demand for emergency services provided by the fire department. Originality/value The authors believe this is the first study applying temporal analysis on a city's emergency incident response data spanning various phases/waves of the COVID-19 pandemic. The analysis may be replicated for other municipal fire services, which can generate further insights that may apply to specific local conditions and states 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 | 0.000 |
| 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 | 0.002 | 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 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".