Publication date: Sep 19, 2023
Since the onset of the COVID-19 pandemic, mathematical models have been widely used to inform public health recommendations regarding COVID-19 control in healthcare settings. To systematically review SARS-CoV-2 transmission models in healthcare settings, and summarize their contributions to understanding nosocomial COVID-19. Systematic search and review. Published articles indexed in PubMed. Modelling studies describing dynamic inter-individual transmission of SARS-CoV-2 in healthcare settings, published by mid-February 2022. Not appropriate for modelling studies. Structured narrative review. Models have mostly focused on acute care and long-term care facilities in high-income countries. Models have quantified outbreak risk, showing great variation across settings and pandemic periods. Regarding surveillance, routine testing rather than symptom-based was highlighted as essential for COVID-19 prevention due to high rates of silent transmission. Surveillance impacts depended critically on testing frequency, diagnostic sensitivity, and turn-around time. Healthcare re-organization also proved to have large epidemiological impacts: beyond obvious benefits of isolating cases and limiting inter-individual contact, more complex strategies (staggered staff scheduling, immune-based cohorting) reduced infection risk. Finally, vaccination impact, while highly effective for limiting COVID-19 burden, varied substantially depending on assumed mechanistic impacts on infection acquisition, symptom onset and transmission. Modelling results form an extensive evidence base that may inform control strategies for future waves of SARS-CoV-2 and other viral respiratory pathogens. We propose new avenues for future models of healthcare-associated outbreaks, with the aim of enhancing their efficiency and contributions to decision-making.