TY - JOUR
AU - King, Marco-Felipe
AU - Wilson, Amanda M.
AU - Weir, Mark H.
AU - López-García, Martín
AU - Proctor, Jessica
AU - Hiwar, Waseem
AU - Khan, Amirul
AU - Fletcher, Louise A.
AU - Sleigh, P. Andrew
AU - Clifton, Ian
AU - Dancer, Stephanie J.
AU - Wilcox, Mark H
AU - Reynolds, Kelly A.
AU - Noakes, Catherine J.
PY - 2021
DA - October
TI - Modeling fomite‐mediated SARS‐CoV‐2 exposure through personal protective equipment doffing in a hospital environment
JO - International Journal of Indoor Environment and Health
VL - 32
IS - 1
DO - https://doi.org/10.1111/ina.12938
AB - Self-contamination during doffing of personal protective equipment (PPE) is a concern for healthcare workers (HCW) following SARS-CoV-2-positive patient care. Staff may subconsciously become contaminated through improper glove removal; so, quantifying this exposure is critical for safe working procedures. HCW surface contact sequences on a respiratory ward were modeled using a discrete-time Markov chain for: IV-drip care, blood pressure monitoring, and doctors’ rounds. Accretion of viral RNA on gloves during care was modeled using a stochastic recurrence relation. In the simulation, the HCW then doffed PPE and contaminated themselves in a fraction of cases based on increasing caseload. A parametric study was conducted to analyze the effect of: (1a) increasing patient numbers on the ward, (1b) the proportion of COVID-19 cases, (2) the length of a shift, and (3) the probability of touching contaminated PPE. The driving factors for the exposure were surface contamination and the number of surface contacts. The results simulate generally low viral exposures in most of the scenarios considered including on 100% COVID-19 positive wards, although this is where the highest self-inoculated dose is likely to occur with median 0.0305 viruses (95% CI =0–0.6 viruses). Dose correlates highly with surface contamination showing that this can be a determining factor for the exposure. The infection risk resulting from the exposure is challenging to estimate, as it will be influenced by the factors such as virus variant and vaccination rates.
PB - Wiley
UR - http://researchrepository.napier.ac.uk/Output/2819149
KW - Coronavirus (COVID-19)
ER