A Validated Framework for Modelling Infectious Disease Spread in Long-Term Healthcare
Bibliographic record
Abstract
This study presents a real-world validation of localized epidemiological modelling techniques using long-term care data, focusing on COVID-19 spread in a complex multi-floor facility. We adapt an advanced agent-based model framework, previously developed for highly localized settings, to address the unique challenges of smart healthcare in long-term care environments. The validation process incorporates quantitative analysis against real-world outbreak data using statistical tests to com-pare probability distributions. Qualitative assessment is further performed on randomly sampled animations. Our methodology addresses computational challenges of simulating large, multi-floor environments by implementing optimized pathfinding algorithms and considering complex disease transmission dynamics. The model accounts for heterogeneous populations of residents, staff, and visitors, each with distinct behavioural patterns and epidemiological responses. Our results found that all simulated outbreak metrics were statistically likely to have been sampled from the same distribution as the validation data. This outcome demonstrates the model’s accuracy in predicting disease spread and its practical relevance in guiding interventions. This study bridges the gap between theoretical modelling and practical application in long-term care settings, providing a validated framework for understanding and managing pandemic scenarios in complex healthcare environments. Our findings have implications beyond the current COVID-19 pandemic and long-term care environments, offering a robust methodology for modelling and managing future infectious disease outbreaks in various healthcare settings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".