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.

Integration of CFD modeling with exposure models for exploring relationships between surface and air transmission routes provides insights into how indoor design and engineering controls influence exposures. One environment in which this integrated methodology is being used is in healthcare. The study objective was to evaluate the influences of differences in healthcare professionals’ (HCPs’) behavior and differences in deposition of norovirus-containing bioaerosols on surfaces for different single patient room layouts and air exchange rates on norovirus accruement on HCP hands. A finite volume Navier Stokes computational fluid dynamics (CFD) model using Lagrangian particle tracking was integrated with a calibrated microbial transfer model and a human behavior model informed by observed mock doctors’ rounds. Viral accruement on hands was estimated for two single patient room set ups, or “room orientations,” where the patient was facing the right side of the room (right-facing) or the left (left-facing). Three air changes per hour (ACH) (10, 6, and 2.5 ACH) and three inlet/outlet scenarios were explored. Viral accruement was compared by room orientation, ACH, and inlet/outlet scenario. The most influential surface on viral accruement on hands was the patient. Greater deposition on the patient occurred when the windows acted as velocity inlets and the door as a pressure outlet (for all 3 ACHs) or when the small windows were velocity inlets and the large window was a pressure outlet (for 6 and 2.5 ACH). When deposition on the patient was different between left- and right-facing rooms, deposition differences drove differences in accruement on hands as opposed to differences in observed behaviors between left- and right-facing rooms. Further modeling expansions include incorporating dose-related behaviours (e.g., self-inoculation), to allow for risk assessment applications.

Healthcare professionals (HCPs) are exposed to highly infectious viruses, such as norovirus, through multiple exposure routes. Understanding exposure mechanisms will inform exposure mitigation interventions. The study objective was to evaluate the influences of hospital patient room layout on differences in HCPs' predicted hand contamination from deposited norovirus particles. Computational fluid dynamic (CFD) simulations of a hospital patient room were investigated to find differences in spatial deposition patterns of bioaerosols for right-facing and left-facing bed layouts under different ventilation conditions. A microbial transfer model underpinned by observed mock care for three care types (intravenous therapy (IV) care, observational care, and doctors' rounds) was applied to estimate HCP hand contamination. Viral accruement was contrasted between room orientation, care type, and by assumptions about whether bioaerosol deposition was the same or variable by room orientation. Differences in sequences of surface contacts were observed for care type and room orientation. Simulated viral accruement differences between room types were influenced by mostly by differences in bioaerosol deposition and by behavior sequences when deposition patterns for the room orientations were similar. Differences between care types were likely driven by differences in hand-to-patient contact frequency, with doctors' rounds resulting in the greatest predicted viral accruement on hands.

BACKGROUND:Healthcare worker (HCW) behaviours, such as the sequence of their contacts with surfaces and hand hygiene moments, are important for understanding disease transmission.AIM:To propose a method for recording sequences of HCW behaviours during mock vs actual procedures, and to evaluate differences for use in infection risk modelling and staff training.METHODS:Procedures for three types of care were observed under mock and actual settings: intravenous (IV) drip care, observational care and doctors' rounds on a respiratory ward in a university teaching hospital. Contacts and hand hygiene behaviours were recorded in real-time using either a handheld tablet or video cameras.FINDINGS:Actual patient care demonstrated 70% more surface contacts than mock care. It was also 2.4 min longer than mock care, but equal in terms of patient contacts. On average, doctors' rounds took 7.5 min (2.5 min for mock care), whilst auxiliary nurses took 4.9 min for observational care (2.4 min for mock care). Registered nurses took 3.2 min for mock IV care and 3.8 min for actual IV care; this translated into a 44% increase in contacts. In 51% of actual care episodes and 37% of mock care episodes, hand hygiene was performed before patient contact; in comparison, 15% of staff delivering actual care performed hand hygiene after patient contact on leaving the room vs 22% for mock care. The number of overall touches in the patient room was a modest predictor of hand hygiene. Using a model to predict hand contamination from surface contacts for Staphylococcus aureus, Escherichia coli and norovirus, mock care underestimated micro-organisms on hands by approximately 30%.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic has increased demands for surgical and respirator masks for healthcare workers (HCWs) and other frontline staff. The debate over the importance of airborne transmission of SARS-CoV-2 continues, but air and laboratory studies have shown that SARS-CoV-2 is viable for >12 h in aerosols [1Chia K. Coleman K. Tan Y. Ong S. Gum M. Lau S. et al.Detection of air and surface contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in hospital rooms of infected patients.medRxiv. 2020; https://doi.org/10.1101/2020.03.29.20046557Crossref Google Scholar, 2Fears A. Klimstra W. Duprex P. Hartman A. Weaver S. Plante K. et al.Comparative dynamic aerosol efficiencies of three emergent coronaviruses and the unusual persistence of SARS-CoV-2 in aerosol suspensions.medRxiv. 2020; https://doi.org/10.1101/2020.04.13.20063784Crossref PubMed Scopus (0) Google Scholar, 3Ong S.W.X. Tan Y.K. Chia P.Y. Lee T.H. Ng O.T. Wong M.S.Y. et al.Air, surface environmental, and personal protective equipment contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from a symptomatic patient.JAMA. 2020; https://doi.org/10.1001/jama.2020.3227Crossref PubMed Scopus (1564) Google Scholar]. Low sampling volumes, location of air outlet fan and potential virus damage during sampling may explain the variability in detection of SARS-CoV-2 [1Chia K. Coleman K. Tan Y. Ong S. Gum M. Lau S. et al.Detection of air and surface contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in hospital rooms of infected patients.medRxiv. 2020; https://doi.org/10.1101/2020.03.29.20046557Crossref Google Scholar,3Ong S.W.X. Tan Y.K. Chia P.Y. Lee T.H. Ng O.T. Wong M.S.Y. et al.Air, surface environmental, and personal protective equipment contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from a symptomatic patient.JAMA. 2020; https://doi.org/10.1001/jama.2020.3227Crossref PubMed Scopus (1564) Google Scholar]. A limited supply of masks creates a risk for the exposure of HCWs to SARS-CoV-2. Non-traditional materials are widely recommended for public use (source control) and have been considered in place of regulated masks in health care, especially in social care settings. While various materials are effective for filtering large droplets, aerosols generated from sneezing, coughing and aerosol-generating procedures may pass more readily through materials or leakage points [4Weber A. Willeke K. Marchioni R. Myojo T. Mckay R. Donnelly J. et al.Aerosol penetration and leakage characteristics of masks used in the health care industry.Am J Infect Control. 1993; 21: 167-173Abstract Full Text PDF PubMed Scopus (134) Google Scholar]. Few data exist on the efficacy of filtration, and no quantitative modelling of efficacies to reduce the risk of infection is currently available. A probabilistic model was developed to estimate the risk of infection for short (30-s, brief patient check) and long (20-min, duration required for patient intubation) inhalation exposure scenarios. These included situations in a room with a patient with coronavirus disease 2019 (COVID-19) when no mask was worn; when an FFP2 (N95) respirator, FFP3 (N99) respirator or surgical mask was worn; or when a non-traditional material mask (silk, tea towel, vacuum cleaner bag, pillowcase, antimicrobial pillowcase, cotton mix, 100% cotton T-shirt, linen or scarf) was worn. Inhaled viral dose was estimated using published concentrations (RNA/m3) of SARS-CoV-2 for >4- and 1–4-μm droplets measured in a hospital setting [1Chia K. Coleman K. Tan Y. Ong S. Gum M. Lau S. et al.Detection of air and surface contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in hospital rooms of infected patients.medRxiv. 2020; https://doi.org/10.1101/2020.03.29.20046557Crossref Google Scholar]. Ranges from reported concentration data originating from a symptomatic and an asymptomatic patient were used to calculate minimum and maximum values for randomly sampled uniform distributions [1Chia K. Coleman K. Tan Y. Ong S. Gum M. Lau S. et al.Detection of air and surface contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in hospital rooms of infected patients.medRxiv. 2020; https://doi.org/10.1101/2020.03.29.20046557Crossref Google Scholar]. Viral exposures for these two size ranges were summed to estimate the total inhaled dose. Doses were estimated for three assumed infectious fractions of total detected viral RNA: 0.1%, 1% and 10%. Inhaled volumes (m3) were estimated using inhalation rates for men and women, where the 5th and 99th percentiles of inhalation rates offered the uniform distribution minimum and maximum, respectively [5U.S. Environmental Protection AgencyExposure factors handbook. US EPA, Washington, DC2011Google Scholar]. Filtration efficacies (fraction of total virus filtered out by the material) were used to model the reduction in viral inhalation exposure for each material type. Due to lack of particle-size-specific filtration efficacy data for these materials, it was assumed that filtration efficacy distributions were applicable to both particle size ranges. For each 10,000 combinations investigated, a filtration efficacy was sampled at random from a normal distribution, left- and right-truncated at 0 and 1, respectively. For surgical masks and non-traditional materials, means and standard deviations (SD) of efficacies were informed by MS2 filtration efficacies [6Davies A. Thompson K.A. Giri K. Kafatos G. Walker J. Bennett A. Testing the efficacy of homemade masks: would they protect in an influenza pandemic?.Disaster Med Public Health Prep. 2013; 7: 413-418Crossref PubMed Scopus (400) Google Scholar]. Mean efficacies of 95% and 99% were assumed for FFP2 and FFP3 respirators, respectively. SDs were provided by Rengasamy et al. (2009), where larger SDs of two manufacturer versions were chosen as a conservative risk approach [7Rengasamy S. Eimer B.C. Shaffer R.E. Comparison of nanoparticle filtration performance of NIOSH-approved and CE-marked particulate filtering facepiece respirators.Ann Occup Hyg. 2009; 53: 117-128Crossref PubMed Scopus (134) Google Scholar]. Data from SARS-CoV and human coronavirus 229E (HCoV-229E) dose–response curves were used to estimate a SARS-CoV-2 exact beta-Poisson curve [8Watanabe T. Bartrand T.A. Weir M.H. Omura T. Haas C.N. Development of a dose–response model for SARS coronavirus.Risk Anal. 2010; 30: 1129-1138Crossref PubMed Scopus (266) Google Scholar]. Based on current epidemiological knowledge, the infectivity of SARS-CoV-2 was assumed to lie between SARS-CoV and HCoV-229E. Pairs of bootstrapped alpha and beta values were used to estimate infection risk per dose. Comparing no protection (baseline) for 20-min and 30-s exposures, it was predicted that the mean risk of infection was reduced by 24–94% and 44–99% depending on the mask. Risk reductions decreased as exposure durations increased. The greatest reduction in estimated mean risk of infection was for FFP3 masks, which reduced baseline mean risks by 94% and 99% for 20-min and 30-s exposures, respectively (Figure 1). Of non-traditional materials, the vacuum cleaner bag resulted in the greatest reduction in mean risk of infection (20-min exposure 58%, 30-s exposure 83%), while scarves offered the lowest reduction (20-min exposure 24%, 30-s exposure 44%) (Figure 1). However, large variability in filtration, such as for silk or the tea towel, should be considered when comparing non-traditional mask materials (Figure 1). Limitations include not accounting for viral transfer from the hands to the mask during mask adjustments, and assuming that all masks were worn in the same way. Realistically, the fit of homemade masks is likely to be more variable than the fit of regulated masks. While the HCoV-229E data utilized for the dose–response curve were based on human data, the SARS-CoV dose–response data originated from an animal-feeding study [8Watanabe T. Bartrand T.A. Weir M.H. Omura T. Haas C.N. Development of a dose–response model for SARS coronavirus.Risk Anal. 2010; 30: 1129-1138Crossref PubMed Scopus (266) Google Scholar]. Future work includes updating the dose–response curve as data on SARS-CoV-2 emerge, and addressing the effects of design/fit on the risk of infection. This study demonstrated that some materials, such as vacuum cleaner bags, may be effective alternatives to reduce the risk of infection. While N95 masks (and similar respirators) are recommended for HCWs and others in close proximity to aerosol-generating procedures, alternative materials may be useful where there are shortages of personal protective equipment (PPE). This may be of particular relevance in low-resource settings where access to PPE is considerably more limited. None declared. A.M. Wilson was supported by the University of Arizona Foundation and the Hispanic Women's Corporation/Zuckerman Family Foundation Student Scholarship Award through the Mel and Enid Zuckerman College of Public Health, University of Arizona. M-F. King and C.J. Noakes were funded by the Engineering and Physical Sciences Research Council, UK: Healthcare Environment Control, Optimisation and Infection Risk Assessment (https://HECOIRA.leeds.ac.uk) (Grant Code: EP/P023312/1). M. López-García was funded by the Medical Research Council, UK (MR/N014855/1). J. Proctor was funded by EPSRC Centre for Doctoral Training in Fluid Dynamics at Leeds (Grant Code EP/L01615X/1). S.E. Abney was funded by a research assistantship from the US-Israel Binational Agricultural Research Development Fund and through a University of Arizona Graduate Access Scholarship.

AbstractSelf-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 risk is critical for safe working procedures. HCW surface contact sequences on a respiratory ward were modelled using a discrete-time Markov chin for: IV-drip care, blood pressure monitoring and doctors’ rounds. Accretion of viral RNA on gloves during care was modelled using a stochastic recurrence relation. The HCW then doffed PPE and contaminated themselves in a fraction of cases based on increasing case load. The risk of infection from this exposure was quantified using a dose-response methodology. A parametric study was conducted to analyse 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 infection risk were surface contamination and number of surface contacts. HCWs on a 100% COVID-19 ward were less than 2-fold more at risk than on a 50% COVID ward (1.6% vs 1%), whilst on a 5% COVID-19 ward, the risk dropped to 0.1% per shift (sd=0.6%). IV-drip care resulted in higher risk than blood pressure monitoring (1.1% vs 1% p<0.0001), whilst doctors’ rounds produced a 0.6% risk (sd=0.8%). Recommendations include supervised PPE doffing procedures such as the “doffing buddy” scheme, maximising hand hygiene compliance post-doffing and targeted surface cleaning for surfaces away from the patient vicinity.ImportanceInfection risk from self-contamination during doffing PPE is an important concern in healthcare settings, especially on a COVID-19 ward. Fatigue during high workload shifts may result in increased frequency of mistakes and hence risk of exposure. Length of staff shift and number of COVID-19 patients on a ward correlate positively with the risk to staff through self-contamination after doffing. Cleaning of far-patient surfaces is equally important as cleaning traditional “high-touch surfaces”, given that there is an additional risk from bioaerosol deposition outside the patient zone(1).