MRSA Transmission in a Personal Care Home Facility: A Spatially Explicit Agent Based Modeling Approach
Bibliographic record
Abstract
The purpose of this study is to estimate the effects of hand washing, cleaning, cohorting, isolation rooms and duration of cleaning on Methicillin Resistant Staphylococcus Aureus (MRSA) colonization and infections. The study uses spatially explicit agent-based modeling approach together with a Susceptible-Exposed-Infected-Recovered (SEIR) to characterize infection spread in an 8-bedroom personal care home facility. The model consists of 8 residents, 2 nurses and 1 cleaner. The model explicitly simulates the dynamics of pathogen reservoirs associated with both surfaces and people, and further counts the colonization and infection events over a 1-year period. To account for stochastics, the model is iterated 100 times for each of five different 'what if' scenarios. Model results suggest that cohorting is the most effective method to reduce the events of MRSA colonization and infections in the personal care home facility. When compared to baseline, cleaning at a higher intensity led to a 35 percent reduction (p-value is less than 0.0005) in the median counts of MRSA colonization (median 202 vs 132 colonization events) and 41 percent reduction (p-value is equal to 0.0335) in the median counts of MRSA infections (median 17 vs 10 MRSA infection events). In terms of intervention that were not effective, we found no statistically significant difference in the efficacy of having duration of cleaning more than 8 hours per day, higher intensity hand washing and use of an isolation room.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".