Year: 2021
Type: preprint
Study design: Observational Study
Abstract: Covid-19 poses significant risk of nosocomial transmission, and preventing this requires good estimates of the basic reproduction number R 0 in hospitals and care facilities, but these are currently lacking. Such estimates are challenging due to small population sizes in these facilities and inconsistent testing practices. We estimate the patient-to-patient R 0 and daily transmission rate of SARS-CoV-2 using data from a closely monitored hospital outbreak in Paris 2020 during the first wave. We use a realistic epidemic model which accounts for progressive stages of infection, stochastic effects and a large proportion of asymptomatic infections. Innovatively, we explicitly include changes in testing capacity over time, as well as the evolving sensitivity of PCR testing at different stages of infection. We conduct rigorous statistical inference using iterative particle filtering to fit the model to the observed patient data and validate this methodology using simulation. We provide estimates for R 0 across the entire hospital (2.6) and in individual wards (from 3 to 15), possibly reflecting heterogeneity in contact patterns or control measures. An obligatory mask-wearing policy introduced during the outbreak is likely to have changed the R 0 , and we estimate values before (8.7) and after (1.3) its introduction, corresponding to a policy efficacy of 85%.
Source: medRxiv
Authors: George Shirreff, Jean-Ralph Zahar, Simon Cauchemez, Laura Temime, Lulla Opatowski, EMEA-MESuRS working group on the nosocomial modelling of SARS-CoV-2
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3202968559
