ArticleBMC infectious diseases2025
An agent-based model to assess the impact of shared staff and occupancy rate on infectious disease burden in nursing homes.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Bayesian inference of nosocomial meticillin-resistant Staphylococcus aureus transmission rates in an urban safety-net hospital.The Journal of hospital infection · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Infectious diseases can propagate between nursing homes through asymptomatic staff members who are employed at multiple facilities. However, the transmission dynamics of infections, both within individual nursing homes and across facilities, has been less investigated. To fill this gap, we developed an agent-based model of two nursing homes extendible to a network of n nursing homes connected with different percentages of shared staff. Focusing on the outbreaks of COVID-19 in U.S. nursing homes, we calibrated the model according to the COVID-19 prevalence data and estimated levels of shared staff for each State. The model simulations indicate that reducing the percentage of shared staff below 5% plays a significant role in controlling the spread of infection from one nursing home to another through personal protective equipment usage, rapid testing, and vaccination. As the percentage of shared staff increases to more than 30%, these measures become less effective, and the mean prevalence of infection reaches a steady state in both nursing homes. The hazard ratios for infection and mortality indicate that nursing homes with higher occupancy rates are more significantly affected by increased staff-sharing percentages. In conclusion, the burden of infection significantly increases with greater staff sharing between nursing homes, particularly in high-occupancy facilities, where transmission dynamics are amplified due to greater resident density and staff interactions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.