Assessing the Role of Daily Activities and Mobility in the Spread of COVID-19 in Montreal With an Agent-Based Approach
Bibliographic record
Abstract
Daily activities and mobility dynamics play a central role in the spread of COVID-19. Close physical interactions involved by certain daily activities help transmit the virus. Travel required by the spatial distribution of activities contributes to the propagation of the virus. In order to control and limit this propagation, it is critical to understand the contribution of daily activities to the dynamics of COVID-19. This paper investigates the connection between daily activities, their distribution in space and time, the characteristics of the individuals performing them, and the transmission of the virus. A business-as-usual agent-based simulation scenario of Montreal, Canada is used. To address this research question, we use two agent-based models: MATSIM and EPISIM. MATSIM simulates daily activities and mobility dynamics of the population. EPISIM simulates the spread of the virus in the population using contact networks computed by MATSIM. A synthetic population of Montreal is defined to replicate the main observed sociodemographic characteristics of Montrealers as well as their activity and mobility patterns. The definition of the synthetic population relies on various data sources: household travel survey, census, real estate, car ownership, and housing data. In the business-as-usual scenario, findings underline the significant role of home, work, and school activities in community transmission of COVID-19. Secondary activities, including leisure and shopping, also help spread the virus, but to a lesser degree in comparison with primary activities. The risk of infection in the workplace depends on the economic sector. Healthcare workers are, by far, the most exposed workers to the virus. Workplace infections mirror the gender-biased job market of Montreal. Most infections in the healthcare and educational services are among women. Most infections in the manufacturing, construction, transportation, and warehousing industries are among men. In the business-as-usual scenario where community transmission is high, primary and secondary school-aged children are found to be a major transmission vector of the virus. Finally, simulation results suggest that the risk of infection in the public transportation system is low.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".