Đánh giá tiềm năng dịch bệnh hô hấp trong các bệnh viện Pháp thông qua thu thập dữ liệu tiếp xúc gần (Tháng 4 - Tháng 6 năm 2020)
Tóm tắt
Từ khóa
#SARS-CoV-2 #rủi ro lây truyền #tiếp xúc gần gũi #dịch tễ học #bệnh việnTài liệu tham khảo
Read, J. M. et al. Hospital-acquired SARS-CoV-2 infection in the UK’s first COVID-19 pandemic wave. Lancet 398, 1037–1038 (2021).
Evans, S. et al. The impact of testing and infection prevention and control strategies on within-hospital transmission dynamics of COVID-19 in English hospitals. Philos. Transact. Royal Soc. B: Biol. Sci. 376, 20200268 (2021).
Temime, L. et al. A conceptual discussion about R0 of SARS-COV-2 in healthcare settings. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciaa682 (2020).
Smith, D. R. M. et al. Optimizing COVID-19 surveillance in long-term care facilities: A modelling study. BMC Med. 18, 386 (2020).
Abbas, M. et al. Nosocomial transmission and outbreaks of coronavirus disease 2019: The need to protect both patients and healthcare workers. Antimicrob. Resist. Infect Control 10, 1 (2021).
Smieszek, T. et al. How should social mixing be measured: Comparing web-based survey and sensor-based methods. BMC Infect. Dis. 14, 136 (2014).
Sick-Samuels, A. C. et al. Improving physical distancing among healthcare workers in a pediatric intensive care unit. Infect Control Hosp. Epidemiol. 1–6. https://doi.org/10.1017/ice.2021.501.
Lucet, J.-C. et al. Electronic sensors for assessing interactions between healthcare workers and patients under airborne precautions. PLOS ONE 7, e37893 (2012).
Hüttel, F. B. et al. Analysis of social interactions and risk factors relevant to the spread of infectious diseases at hospitals and nursing homes. PLOS ONE 16, e0257684 (2021).
Isella, L. et al. Close encounters in a pediatric ward: Measuring face-to-face proximity and mixing patterns with wearable sensors. PLOS ONE 6, e17144 (2011).
Vanhems, P. et al. Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PLOS ONE 8, e73970 (2013).
Hertzberg, V. S. et al. Contact networks in the emergency department: Effects of time, environment, patient characteristics, and staff role. Soc. Netw. 48, 181–191 (2017).
Duval, A. et al. Measuring dynamic social contacts in a rehabilitation hospital: Effect of wards, patient and staff characteristics. Sci. Rep. 8, 1686 (2018).
Wölfel, R. et al. Virological assessment of hospitalized patients with COVID-2019. Nature 581, 465–469 (2020).
Poletti, P. et al. Seroprevalence of and risk factors associated with SARS-CoV-2 infection in health care workers during the early COVID-19 pandemic in Italy. JAMA Netw. Open 4, e2115699 (2021).
Serafino, M. et al. Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization. PLOS Comput. Biol. 18, e1009865 (2022).
Ge, Y. et al. COVID-19 transmission dynamics among close contacts of index patients with COVID-19: A population-based cohort study in Zhejiang Province, China. JAMA Internal Med. 181, 1343–1350 (2021).
Lindsey, B. B. et al. Characterising within-hospital SARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves. Nat. Commun. 13, 671 (2022).
Crawford, C. et al. Modeling of aerosol transmission of airborne pathogens in ICU rooms of COVID-19 patients with acute respiratory failure. Sci. Rep. 11, 11778 (2021).
Allen, J. G. & Ibrahim, A. M. Indoor air changes and potential implications for SARS-CoV-2 transmission. JAMA 325, 2112–2113 (2021).
Robles-Romero, J. M., Conde-Guillén, G., Safont-Montes, J. C., García-Padilla, F. M. & Romero-Martín, M. Behaviour of aerosols and their role in the transmission of SARS-CoV-2; a scoping review. Rev. Med. Virol. 32, 2297. https://doi.org/10.1002/rmv.2297 (2021).
Yang, F., Pahlavan, A. A., Mendez, S., Abkarian, M. & Stone, H. A. Towards improved social distancing guidelines: Space and time dependence of virus transmission from speech-driven aerosol transport between two individuals. Phys. Rev. Fluids 5, 122501 (2020).
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2022).