Effects of face masks and ventilation on the risk of SARS-CoV-2 respiratory transmission in public toilets: a quantitative microbial risk assessment

AbstractPublic toilets could increase the risk of COVID-19 infection via airborne transmission; however, related research is limited. We aimed to estimate SARS-CoV-2 infection risk through respiratory transmission using a quantitative microbial risk assessment framework by retrieving SARS-CoV-2 concentrations from the swab tests of 251 Thai patients. Three virus-generating scenarios were investigated: an infector breathing, breathing with a cough, and breathing with a sneeze. Infection risk (97.5th percentile) was as high as 10−3 with breathing and increased to 10−1 with a cough or sneeze, thus all higher than the risk benchmark of 5 × 10−5 per event. No significant gender differences for toilet users (receptors) were noted. The highest risk scenario of breathing and a sneeze was further evaluated for risk mitigation measures. Risk mitigation to lower than the benchmark succeeded only when the infector and receptor simultaneously wore an N95 respirator or surgical mask and when the receptor wore an N95 respirator and the infector wore a denim fabric mask. Ventilation up to 20 air changes per hour (ACH), beyond the 12-ACH suggested by the WHO, did not mitigate risk. Virus concentration, volume of expelled droplets, and receptor dwell time were identified as the main contributors to transmission risk.Highlights-The use of public toilets poses a risk of SARS-CoV-2 respiratory transmission-Highest risks generated in the order of sneezing, coughing, and breathing-No gender differences in risk by counteracting dwell times and inhalation rates-Ventilation did not reduce risk even at 20 ACH, beyond the WHO-recommended value-N95 and surgical masks offer the most effective risk mitigation to toilet usersGraphical abstract

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Methods for Handling Left-Censored Data in Quantitative Microbial Risk Assessment

Applied and Environmental Microbiology ◽

10.1128/aem.01203-18 ◽

2018 ◽

Vol 84 (20) ◽

Cited By ~ 10

Author(s):

Robert A. Canales ◽

Amanda M. Wilson ◽

Jennifer I. Pearce-Walker ◽

Marc P. Verhougstraete ◽

Kelly A. Reynolds

Keyword(s):

Risk Assessment ◽

Censored Data ◽

Limit Of Detection ◽

Environmental Microbiology ◽

Infection Risk ◽

Data Sets ◽

Quantitative Microbial Risk Assessment ◽

Data Set ◽

Microbial Risk Assessment ◽

Microbial Risk

ABSTRACTData below detection limits, left-censored data, are common in environmental microbiology, and decisions in handling censored data may have implications for quantitative microbial risk assessment (QMRA). In this paper, we utilize simulated data sets informed by real-world enterovirus water data to evaluate methods for handling left-censored data. Data sets were simulated with four censoring degrees (low [10%], medium [35%], high [65%], and severe [90%]) and one real-life censoring example (97%) and were informed by enterovirus data assuming a lognormal distribution with a limit of detection (LOD) of 2.3 genome copies/liter. For each data set, five methods for handling left-censored data were applied: (i) substitution with LOD/√2, (ii) lognormal maximum likelihood estimation (MLE) to estimate mean and standard deviation, (iii) Kaplan-Meier estimation (KM), (iv) imputation method using MLE to estimate distribution parameters (MI method 1), and (v) imputation from a uniform distribution (MI method 2). Each data set mean was used to estimate enterovirus dose and infection risk. Root mean square error (RMSE) and bias were used to compare estimated and known doses and infection risks. MI method 1 resulted in the lowest dose and infection risk RMSE and bias ranges for most censoring degrees, predicting infection risks at most 1.17 × 10−2from known values under 97% censoring. MI method 2 was the next overall best method. For medium to severe censoring, MI method 1 may result in the least error. If unsure of the distribution, MI method 2 may be a preferred method to avoid distribution misspecification.IMPORTANCEThis study evaluates methods for handling data with low (10%) to severe (90%) left-censoring within an environmental microbiology context and demonstrates that some of these methods may be appropriate when using data containing concentrations below a limit of detection to estimate infection risks. Additionally, this study uses a skewed data set, which is an issue typically faced by environmental microbiologists.

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