SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study
Huang, Qimin et al. (2020), SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study, Dryad, Dataset, https://doi.org/10.5061/dryad.sbcc2fr4w
Background: Development of strategies for mitigating the severity of COVID-19 is now a top public health priority. We sought to assess strategies for mitigating the COVID-19 outbreak in a hospital setting via the use of non-pharmaceutical interventions.
Methods: We developed an individual-based model for COVID-19 transmission in a hospital setting. We calibrated the model using data of a COVID-19 outbreak in a hospital unit in Wuhan. The calibrated model was used to simulate different intervention scenarios and estimate the impact of different interventions on outbreak size and workday loss.
Findings: The use of high efficacy facial masks was shown to be able to reduce infection cases and workday loss by 80% (90% CrI: 73.1% - 85.7%) and 87% (CrI: 80.0% - 92.5%), respectively. The use of social distancing alone, through reduced contacts between healthcare workers, had a marginal impact on the outbreak. Our results also indicated that a quarantine policy should be coupled with other interventions to achieve its effect. The effectiveness of all these interventions was shown to increase with their early implementation.
Conclusions: Our analysis shows that a COVID-19 outbreak in a hospital’s non-COVID-19 unit can be controlled or mitigated by the use of existing non-pharmaceutical measures.
Dataset was collected by questionnaire. Written informed consent was required before the data collecting, and participants were informed that they could refuse to answer any question. The questionnaire did not ask about infection status, and no biological samples were collected.
Individual-based model setup and hospital aggregated data. The code is implemented in Wolfram Mathematica, it can be opened and operated with Mathematica only.
Fundamental Research Funds for the Central Universities, Award: 2020kfyXGYJ010
National Science Foundation RAPID, Award: DEB-2028631