ArticleRoyal Society open science2023
Deconstructing the effects of stochasticity on transmission of hospital-acquired infections in ICUs.
Article in Royal Society open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
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Who cites it
3 citing papers in PubMed.
- Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settings.NPJ digital medicine · 2025Article
- Identifying and Forecasting Importation and Asymptomatic Spreaders of Multi-drug Resistant Organisms in Hospital Settings.medRxiv : the preprint server for health sciences · 2025Article
- Deconstructing the effects of stochasticity on transmission of hospital-acquired infections in ICUs.Royal Society open science · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
The inherent stochasticity in transmission of hospital-acquired infections (HAIs) has complicated our understanding of transmission pathways. It is particularly difficult to detect the impact of changes in the environment on acquisition rate due to stochasticity. In this study, we investigated the impact of uncertainty (epistemic and aleatory) on nosocomial transmission of HAIs by evaluating the effects of stochasticity on the detectability of seasonality of admission prevalence. For doing so, we developed an agent-based model of an ICU and simulated the acquisition of HAIs considering the uncertainties in the behaviour of the healthcare workers (HCWs) and transmission of pathogens between patients, HCWs, and the environment. Our results show that stochasticity in HAI transmission weakens our ability to detect the effects of a change, such as seasonality patterns, on acquisition rate, particularly when transmission is a low-probability event. In addition, our findings demonstrate that data compilation can address this issue, while the amount of required data depends on the size of the said change and the degree of uncertainty. Our methodology can be used as a framework to assess the impact of interventions and provide decision-makers with insight about the minimum required size and target of interventions in a healthcare facility.
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Registered trials
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