Many infectious diseases, including COVID-19, are transmitted by airborne pathogens. There is a need for effective environmental control measures which, ideally, are not reliant on human behaviour. One potential solution is Krypton Chloride (KrCl) excimer lamps (often referred to as Far-UVC), which can efficiently inactivate pathogens, such as coronaviruses and influenza, in air. Research demonstrates that when KrCl lamps are filtered to remove longer-wavelength ultraviolet emissions they do not induce acute reactions in the skin or eyes, nor delayed effects such as skin cancer. While there is laboratory evidence for Far-UVC efficacy, there is limited evidence in full-sized rooms. For the first time, we show that Far-UVC deployed in a room-sized chamber effectively inactivates aerosolised Staphylococcus aureus. At a room ventilation rate of 3 air-changes-per-hour (ACH), with 5 filtered-sources the steady-state pathogen load was reduced by 98.4% providing an additional 184 equivalent air changes (eACH). This reduction was achieved using Far-UVC irradiances consistent with current American Conference of Governmental Industrial Hygienists threshold limit values for skin for a continuous 8-h exposure. Our data indicate that Far-UVC is likely to be more effective against common airborne viruses, including SARS-CoV-2, than bacteria and should thus be an effective and “hands-off” technology to reduce airborne disease transmission. The findings provide room-scale data to support the design and development of effective Far-UVC systems.

Airborne microorganisms in hospitals have been associated with several hospital‐acquired infections (HAIs), and various measures of indoor air quality (IAQ) parameters such as temperature, relative humidity, carbon dioxide (CO2), particle mass concentration, and particle size have been linked to pathogen survival or mitigation of pathogen spread. To investigate whether there are quantitative relationships between the concentration of airborne microorganisms and the IAQ in the hospital environment. Web of Science, Scopus and PubMed databases were searched for studies reporting airborne microbial levels and any IAQ parameter(s) in hospital environments, from database inception to October 2020. Pooled effect estimates were determined via random‐effects models. Seventeen of 654 studies were eligible for the meta‐analysis. The concentration of airborne microbial measured as aerobic colony count (ACC) was significantly correlated with temperature (r = 0.25 [95% CI = 0.06–0.42], p = 0.01), CO2 concentration (r = 0.53 [95% CI = 0.40–0.64], p ˂ 0.001), particle mass concentration (≤5 µg/m3; r = 0.40 [95% CI = 0.04–0.66], p = 0.03), and particle size (≤5 and ˃5 µm), (r = 0.51 [95% CI = 0.12–0.77], p = 0.01 and r = 0.55 [95% CI = 0.20–0.78], p = 0.003), respectively, while not being significantly correlated with relative humidity or particulate matter of size >5 µm. Conversely, airborne total fungi (TF) were not significantly correlated with temperature, relative humidity, or CO2 level. However, there was a significant weak correlation between ACC and TF (r = 0.31 [95% CI = 0.07–0.52], p = 0.013). Although significant correlations exist between ACC and IAQ parameters, the relationship is not definitive; the IAQ parameters may affect the microorganisms but are not responsible for the presence of airborne microorganisms. Environmental parameters could be related to the generating source, survival, dispersion, and deposition rate of microorganisms. Future studies should record IAQ parameters and factors such as healthcare worker presence and the activities carried out such as cleaning, sanitizing, and disinfection protocols. Foot traffic would influence both the generation of microorganisms and their deposition rate onto surfaces in the hospital environment. These data would inform models to improve the understanding of the likely concentration of airborne microorganisms and provide an alternative approach for real‐time monitoring of the healthcare environment.

Bacterial transmission from contaminated surfaces via hand contact plays a critical role in disease spread. However, the fomite‐to‐finger transfer efficiency of microorganisms during multiple sequential surface contacts with and without gloves has not been formerly investigated. We measured the quantity of Escherichia coli on fingertips of participants after 1‐8 sequential contacts with inoculated plastic coupons with and without nitrile gloves. A Bayesian approach was used to develop a mechanistic model of pathogen accretion to examine finger loading as a function of the difference between E coli on surfaces and fingers. We used the model to determine the coefficient of transfer efficiency (λ), and influence of swabbing efficiency and finger area. Results showed that λ for bare skin was higher (49%, 95% CI = 32%‐72%) than for gloved hands (30%, CI = 17%‐49%). Microbial load tended toward a dynamic equilibrium after four and six contacts for gloved hands and bare skin, respectively. Individual differences between volunteers’ hands had a negligible effect compared with use of gloves (P < .01). Gloves reduced loading by 4.7% (CI = −12%‐21%) over bare skin contacts, while 20% of participants accrued more microorganisms on gloved hands. This was due to poor fitting, which created a larger finger surface area than bare hands.

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 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.

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.