A Discrete-event modeling method to study human behavior for spread of diseases on university campuses
Bibliographic record
Abstract
• Architecture to study diffusion processes in multiplex networks applied to diseases. • Discrete Event System Specification used for parameterized model. • CO2 concentration in enclosed spaces used as proxy for airborne diseases. • Modeling spread of disease in workplaces accounting for human behavior. • University campus case study tests disease spread mitigation policy. The COVID-19 pandemic has highlighted the importance of defining sound policies to make attending workplaces safer. Sometimes, deciding on different policies is challenging as this highly depends on the behavior of the individuals. This research introduces a Discrete Event-based methodology and a prototype implementation to study such policies, including human behavior along with information about the workplace layout and building characteristics such as ventilation rate or room capacity. The method is based on a combination of agent-based models, diffusion processes and discrete-event simulation. We exemplify how to use this method using a case study based on Carleton University’s Campus, in which we use the methodology and tools to study the effect of ventilation, as well as the application of a policy where sick students are denied entry to the campus on the number of disease cases on campus.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".