Geocomputational Approach to Simulate and Understand the Spatial Dynamics of COVID-19 Spread in the City of Montreal, QC, Canada
Bibliographic record
Abstract
Throughout history, pandemics have forced societies to think beyond typical management and control protocols. The main goals of this study were to simulate and understand the spatial dynamics of COVID-19 spread and assess the efficacy of two policy measures in Montreal, Canada, to mitigate the COVID-19 outbreak. We simulated the COVID-19 outbreak using a Geographical Information System (GIS)-based agent-based model (ABM) and two management scenarios as follows: (1) human mobility reduction; and (2) observation of self-isolation. The ABM description followed the ODD (Overview, Design concepts, Details) protocol. Our simulation experiments indicated that the mainstream of COVID-19 transmissions (i.e., approximately 90.34%) occurred in public places. Besides, the results indicated that the rules aiming to reduce population mobility, led to a reduction of about 63 infected people each week, on average. Furthermore, our scenarios revealed that if instead of 42% (i.e., the adjusted value in the calibration), 10%, 20%, and 30% of infectious people had followed the self-isolation measure, the number of infected people would have risen by approximately 259, 207, and 83 more each week, on average, respectively. The map of critical locations of COVID-19 spreading resulted from our modeling and the evaluated effectiveness of two control measures on the COVID-19 outbreak could assist health policymakers to navigate through the pandemic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".