Assessing the Risk of Spatial Spreading of Diseases in Hospitals
Bibliographic record
Abstract
In recent years, the transmission of healthcare-associated infections (HAIs) has led to substantial economic loss, extensive damage, and many preventable deaths. With the increasing availability of data, mathematical models of pathogen spreading in healthcare settings are becoming more detailed and realistic. Here, we make use of spatial and temporal information that has been obtained from healthcare workers (HCWs) in three hospitals in Canada and generate data-driven networks that allow us to realistically simulate the spreading of an airborne respiratory pathogen in such settings. By exploring in depth the dynamics of HAIs on the generated networks, we quantify the infection risk associated with both the spatial units of the hospitals and HCWs categorized by their occupations. Our findings show that the “inpatient care” and “public area” are the riskiest categories of units and “nurse” is the occupation at a greater risk of getting infected. Our results provide valuable insights that can prove important for measuring risks associated with HAIs and for strengthening prevention and control measures with the potential to reduce transmission of infections in hospital 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".