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Aims The aim of this study was to determine how the transfer efficiency of MS-2 coliphage from the toilet seat to hands and fingertip to lip differs according to the suspension of the inoculum. Methods and Results Hands were sampled after lifting a toilet seat which was inoculated with MS-2 on the underneath side. MS-2 was suspended in a spectrum of proteinaceous and non-proteinaceous solutions. Transfer efficiencies were greatest with the ASTM tripartite soil load (3.02% ± 4.03) and lowest with phosphate-buffered saline (PBS) (1.10% ± 0.81) for hand-to-toilet seat contacts. Finger-to-lip transfer rates were significantly different (p < 0.05) depending on suspension matrix, with PBS yielding the highest transfer (52.53% ± 4.48%) and tryptose soy broth (TSB) the lowest (23.15% ± 24.27%). Quantitative microbial risk assessment was used to estimate the probability of infection from adenovirus and norovirus from finger contact with a toilet seat. Conclusions The greatest transfer as well as the largest variation of transfer were measured for finger-to-lip contacts as opposed to toilet seat-to-finger contacts. These factors influence the estimation of the probability of infection from micro-activity, that is, toilet seat adjustment. Significance and Impact Viruses may be transferred from various human excreta with differing transfer efficiencies, depending on the protein content.

School nurses and unlicensed assistive personnel (UAPs) are essential to the health and wellness of school children. However, most US schools do not have a full-time licensed nurse. During the COVID-19 pandemic, school nurses and UAPs have been integral in ensuring that the health needs of students were met. They have seen a marked increase in their responsibilities included implementing COVID-19 mitigation strategies, screening for symptoms, testing students and staff, conducting contact tracing and data collection, and ensuring the implementation of rapidly changing COVID-19 guidelines and protocols for schools. The objective of this study was to explore COVID-19 occupational changes and their contributions to stress among school nurses and UAPs through a content analysis of local and national media articles. A Google search of articles published between February 2020 and September 2021 was conducted using the following search terms: ‘school nurse’, ‘COVID-19’, ‘health aide’, ‘stress’, and ‘experiences’. A search was also conducted in Nexis Uni. Articles were included if the topic discussed school nurses or UAPs and COVID-19. All articles that examined nurses in other settings were excluded from the review. We examined topics and themes temporally (from February 2020 to September 2021) and spatially (i.e. the frequency by US state). Overall, 496 media articles discussing school nurses and COVID-19 were included in our review. The highest volume of articles was from September 2021 (22%, 111/496). Other months with relatively high volume of articles included August 2020 (9%, 43/496), January 2021 (10%, 47/496), February 2021 (9%, 44/496), and August 2021 (8%, 39/496). These larger article volumes coincided with notable COVID-19 events, including returning to school in the fall (August 2020 and August 2021), school nurses assisting with vaccine rollouts among adults in the USA (January/February 2021), concerns regarding the delta variant (August/September 2021), and vaccine rollouts for children ages 12–15 (September 2021). The representation of articles spatially (national, state, regional, or local) was 66 (13%) articles at national level, 217 (44%) state level, 25 (5%) regional level, and 188 (38%) local news at the city and/or village level. Pennsylvania had the highest frequency of articles, but when standardized to the state population, Alaska had the highest rate of media per 100 000 people. Three major themes were identified in our analysis: (i) safety; (ii) pandemic-related fatigue/stress; and (iii) nursing shortage/budget. The most represented theme for articles before September 2021 was that of safety. Over time, the themes of pandemic-related fatigue/stress and nursing shortage/budget increased with the most notable increase being in September 2021. The COVID-19 pandemic has resulted in new occupational risks, burdens, and stressors experienced by school nurses and UAPs. School nurses play a critical role in disease surveillance, disaster preparedness, wellness and chronic disease prevention interventions, immunizations, mental health screening, and chronic disease education. Furthermore, they provide a safety net for our most vulnerable children. Given that school nurses were already over-burdened and under-resourced prior to the pandemic, characterization of these new burdens and stressors will inform emergency preparedness resources for school health personnel during future pandemics or outbreaks.

Background: Nurses face the risk of new onset occupational asthma (OA) due to exposures to cleaning and disinfection (C&D) agents used to prevent infections in healthcare facilities. The objective of this study was to measure nurses’ preferences when presented with simultaneous OA and respiratory viral infection (e.g., COVID-19) risks related to increased/decreased C&D activities. Methods: Nurses working in healthcare for ≥1 year and without physician-diagnosed asthma were recruited for an online anonymous survey, including four risk–risk tradeoff scenarios between OA and respiratory infection with subsequent recovery (Infect and Recovery) or subsequent death (Infect and Death). Nurses were presented with baseline risks at hypothetical “Hospital 1”, and were asked to choose Hospital 2 (increased OA risk to maintain infection risk), Hospital 3 (increased infection risk to maintain OA risk), or indicate that they were equally happy. Results: Over 70% of nurses were willing to increase infection risk to maintain baseline OA risk if they were confident they would recover from the infection. However, even when the risk of infection leading to death was much lower than OA, most nurses were not willing to accept a larger (but still small) risk of death to avoid doubling their OA risk. Age, work experience, and ever having contracted or knowing anyone who has contracted a respiratory viral infection at work influenced choices. Conclusions: We demonstrate the novel application of a risk–risk tradeoff framework to address an occupational health issue. However, more data are needed to test the generalizability of the risk preferences found in this specific risk–risk tradeoff context.

Aerosolization of SARS-CoV-2 by COVID-19 patients can put healthcare workers and susceptible individuals at risk of infection. Air sampling for SARS-CoV-2 has been conducted in healthcare settings, but methods vary widely and there is need for improvement. The objective of this study was to evaluate the feasibility of using a high-volume filter sampler, BioCapture z720, to detect SARS-CoV-2 in COVID-19 patient rooms in a medical intensive care unit, a dedicated COVID-19 ward, and at nurses’ stations. In some locations, the BioSpot-VIVAS, known for high efficiency in the collection of virus-containing bioaerosols, was also operated. The samples were processed for SARS-CoV-2 RNA with multi-plex nested polymerase chain reaction. One of 28 samples collected with the high-volume filter sampler was positive for SARS-CoV-2; all 6 samples collected with BioSpot-VIVAS were negative for SARS-CoV-2. The high-volume filter sampler was more portable and less intrusive in patient rooms than the BioSpot-VIVAS, but limits of detection remain unknown for this device. This study will inform future work to evaluate the reliability of these types of instruments and inform best practices for their use in healthcare environments for SARS-CoV-2 air sampling.

Background When modeling exposures from contact with fomites, there are many choices in defining the sizes of compartments representing environmental surfaces and hands, and the portions of compartments involved in contacts. These choices impact dose estimates, yet there is limited guidance for selection of these model parameters. Objective The study objective was to explore methods for representing environmental surface and hand contact areas in exposure models and implications for estimated doses. Methods A simple scenario was used: an individual using their hands to contact their face and two microbially contaminated environmental surfaces. Four models were developed to explore different compartmentalization strategies: (1) hands and environmental surfaces each represented by one compartment, (2) hands represented by two compartments (fingertips vs. non-fingertip areas) while environmental surfaces were represented by one compartment, (3) hands represented by a single compartment and environmental surfaces represented by two compartments, and (4) hands and environmental surfaces each represented by two compartments. Sensitivity analyses were conducted to evaluate the influence of heterogeneous surface contact frequency, hand contact type, and hand dominance on dose. Results Estimated doses were greatest when hand areas and environmental surfaces were each represented by two compartments, indicating that surface area “dilutes” contaminant concentration and decreases estimated dose. Significance Model compartment designations for hands and environmental surfaces affect dose estimation, but more human behavior data are needed. Impact statement A common problem for exposure models describing exposures via hand-to-surface contacts occurs in the way that estimated contamination across human skin (usually hands) or across environmental surfaces is spatially averaged, as opposed to accounting for concentration changes across specific parts of the hand or individual surfaces. This can lead to the dilution of estimated contaminants and biases in estimated doses in risk assessments. The magnitude of these biases and implications for the accuracy in risk assessments are unknown. We quantify differences in dose for various strategies of compartmentalizing environmental surfaces and hands to inform guidance on future exposure model development.

Background Bluetooth-based exposure notification apps can supplement manual contact tracing to reduce SARS-CoV-2 transmission. Their speed, scalability, and privacy preservation are generally acknowledged, but less exploited are smartphones’ accurate measurement of duration, and ability to automatically calculate risk from multiple inputs. Methods We model uncertainty in the shape of an exhaled virus-containing plume, inhalation parameters, and distance as a function of Bluetooth attenuation. We assume relative rates of viral shedding depend on the timing of exposure relative to symptom onset. We calibrate an exponential dose-response curve on the basis of the infection probabilities of household contacts. The conditional probability of current or future infectiousness, conditioned on how long post-exposure an exposed individual has been free of symptoms, decreases during quarantine, with shape determined by the distribution of incubation periods, proportion of asymptomatic cases, and distribution of asymptomatic shedding durations. It can be adjusted for negative test results using Bayes Theorem. Findings As an example of our calculations, fifteen minutes of close contact with a high-shedding individual, given a 15% asymptomatic infection rate and no testing, would require 5- and 14-day quarantine for their risk of current or future infectiousness to fall below 0.84% and 0.14% risk, respectively. Interpretation The Covid-Watch app is currently programmed either to use a threshold on initial infection risk to determine 14-day quarantine onset, or on the conditional probability of current and future infectiousness conditions to determine both quarantine and duration. Either threshold can be set by public health authorities. Funding University of Arizona

Despite the widely acknowledged public health impacts of surface water fecal contamination, there is limited understanding of seasonal effects on (i) fate and transport processes and (ii) the mechanisms by which they contribute to water quality impairment. Quantifying relationships between land use, chemical parameters, and fecal bacterial concentrations in watersheds can help guide the monitoring and control of microbial water quality and explain seasonal differences. The goals of this study were to (i) identify seasonal differences in Escherichia coli and Bacteroides thetaiotaomicron concentrations, (ii) evaluate environmental drivers influencing microbial contamination during baseflow, snowmelt, and summer rain seasons, and (iii) relate seasonal changes in B. thetaiotaomicron to anticipated gastrointestinal infection risks. Water chemistry data collected during three hydroclimatic seasons from 64 Michigan watersheds were analyzed using seasonal linear regression models with candidate variables including crop and land use proportions, prior precipitation, chemical parameters, and variables related to both wastewater treatment and septic usage. Adaptive least absolute shrinkage and selection operator (LASSO) linear regression with bootstrapping was used to select explanatory variables and estimate coefficients. Regardless of season, wastewater treatment plant and septic system usage were consistently selected in all primary models for B. thetaiotaomicron and E. coli. Chemistry and precipitation-related variable selection depended upon season and organism. These results suggest a link between human pollution (e.g., septic systems) and microbial water quality that is dependent on flow regime.

Background Contributions of contaminated wheelchairs to nosocomial pathogen transmission are relatively unknown. Our aim was to develop a model predicting pathogen exposures for patients utilizing wheelchairs and estimate exposure reduction potential of wheelchair disinfection between rides. Methods An agent-based model was informed by wheelchair location data from a connected 215-bed acute care and 250-bed long-term care facility. Simulated scenarios varied in frequencies of patient wheelchair contamination and wheelchair disinfection in between trips. Clostridioides difficile and methicillin-resistant Staphylococcus aureus concentrations on patient hands at the end of wheelchair trips were estimated. Exposure reductions due to disinfection, assuming low real-world efficacies (50%, 70%, and 90%), were compared. Results In the simulation, when few patients introduced contamination to wheelchairs, disinfection in between patients 50% of the time decreased baseline (no disinfection) estimated exposures for the 50th wheelchair rider by >99.999%. When patients had a 50% chance of being contaminated before the wheelchair ride, disinfection did not reduce exposures consistently. Discussion The efficacy of disinfection in between patient rides as an exposure mitigation strategy likely depends on the frequency of infected patient wheelchair use. Conclusions During outbreak, high contamination conditions, disinfection, alone, is not enough to protect patients from wheelchair-mediated exposures.

Hand-to-face contacts are important for estimating chemical and microbial exposures. Few studies describe children’s hand-to-eye or -nose contacts or adults’ hand-to-face contacts. The study objective was to characterize hand-to-head (mouth, eyes, nose, and other) contacts for children in a daycare and adults in multiple locations. Macro-activities and sequences of hand-to-face contacts were recorded for 263 people observed for 30 min each. Statistically significant differences between locations, males and females, adults and children, and during eating and non-eating macro-activities were evaluated. Discrete Markov chains were fit to observed contact sequences and compared among adults and children during eating and non-eating macro-activities. No significant differences in contact frequency were observed between males and females with the exception of hand-to-nose contacts. Children tended to touch the mouth, eyes, and nose more frequently than adults during non-eating macro-activities. Significant differences in contact frequency were observed between locations. Transitional probabilities indicated that children make repetitive mouth, eye, and nose contacts while adults frequently transition to contacts of the head other than the mouth, eyes, or nose. More data are needed to evaluate the effect of age on adults’ contact frequencies and to confirm lack of statistically significant differences between adults and children during eating macro-activities.

Exposure models are useful tools for relating environmental monitoring data to expected health outcomes. The objective of this study was to (1) compare two Legionella shower exposure models, and (2) develop a risk calculator tool for relating environmental monitoring data to estimated Legionella infection risks and Legionnaires' Disease (LD) illness risks. Legionella infection risks for a single shower event were compared using two shower Legionella exposure models. These models varied in their description of partitioning of Legionella in aerosols and aerosol deposition in the lung, where Model 1 had larger and fewer aerosol ranges than Model 2. Model 2 described conventional vs. water efficient showers separately, while Model 1 described exposure for an unspecified shower type (did not describe it as conventional or water efficient). A Monte Carlo approach was used to account for variability and uncertainty in these aerosolization and deposition parameters, Legionella concentrations, and the dose-response parameter. Methods for relating infection risks to illness risks accounting for demographic differences were used to inform the risk calculator web application ("app"). Model 2 consistently estimated higher infection risks than Model 1 for the same Legionella concentration in water and estimated deposited doses with less variability. For a 7.8-min shower with a Legionella concentration of 0.1 CFU/mL, the average infection risks estimated using Model 2 were 4.8 × 10

Viral infections are an occupational health concern for office workers and employers. The objectives of this study were to estimate rotavirus, rhinovirus, and influenza A virus infection risks in an office setting and quantify infection risk reductions for two hygiene interventions. In the first intervention, research staff used an ethanol-based spray disinfectant to clean high-touch non-porous surfaces in a shared office space. The second intervention included surface disinfection and also provided workers with alcohol-based hand sanitizer gel and hand sanitizing wipes to promote hand hygiene. Expected changes in surface concentrations due to these interventions were calculated. Human exposure and dose were simulated using a validated, steady-state model incorporated into a Monte Carlo framework. Stochastic inputs representing human behavior, pathogen transfer efficiency, and pathogen fate were utilized, in addition to a mixed distribution that accounted for surface concentrations above and below a limit of detection. Dose-response curves were then used to estimate infection risk. Estimates of percent risk reduction using mean values from baseline and surface disinfection simulations for rotavirus, rhinovirus, and influenza A infection risk were 14.5%, 16.1%, and 32.9%, respectively. For interventions with both surface disinfection and the promotion of personal hand hygiene, reductions based on mean values of infection risk were 58.9%, 60.8%, and 87.8%, respectively. This study demonstrated that surface disinfection and the use of personal hand hygiene products can help decrease virus infection risk in communal offices. Additionally, a variance-based sensitivity analysis revealed a greater relative importance of surface concentrations, assumptions of relevant exposure routes, and inputs representing human behavior in estimating risk reductions.

Mathematical models describing indirect contact transmission are an important component of infectious disease mitigation and risk assessment. A model that tracks microorganisms between compartments by coupled ordinary differential equations or a Markov chain is benchmarked against a mechanistic interpretation of the physical transfer of microorganisms from surfaces to fingers and subsequently to a susceptible person's facial mucosal membranes. The primary objective was to compare these models in their estimates of doses and changes in microorganism concentrations on hands and fomites over time. The abilities of the models to capture the impact of episodic events, such as hand hygiene, and of contact patterns were also explored. For both models, greater doses were estimated for the asymmetrical scenarios in which a more contaminated fomite was touched more often. Differing representations of hand hygiene in the Markov model did not notably impact estimated doses but affected pathogen concentration dynamics on hands. When using the Markov model, losses due to hand hygiene should be handled as separate events as opposed to time-averaging expected losses. The discrete event model demonstrated the effect of hand-to-mouth contact timing on the dose. Understanding how model design influences estimated doses is important for advancing models as reliable risk assessment tools.

Highlights • We used quantitative microbial risk assessment to relate surface disinfection efficacy to COVID-19 infection risk. • Little log10 reductions may be needed for low viral bioburden conditions. • >2 log10 may be needed to achieve risks, We used a quantitative microbial risk assessment approach to relate log10 disinfection reductions of SARS-CoV-2 bioburden to COVID-19 infection risks. Under low viral bioburden, minimal log10 reductions may be needed to reduce infection risks for a single hand-to-fomite touch to levels lower than 1:1,000,000, as a risk comparison point. For higher viral bioburden conditions, log10 reductions of more than 2 may be needed to achieve median infection risks of less than 1:1,000,000.

Understanding healthcare viral disease transmission and the effect of infection control interventions will inform current and future infection control protocols. In this study, a model was developed to predict virus concentration on nurses' hands using data from a bacteriophage tracer study conducted in Tucson, Arizona, in an urgent care facility. Surfaces were swabbed 2 hours, 3.5 hours, and 6 hours postseeding to measure virus spread over time. To estimate the full viral load that would have been present on hands without sampling, virus concentrations were summed across time points for 3.5- and 6-hour measurements. A stochastic discrete event model was developed to predict virus concentrations on nurses' hands, given a distribution of virus concentrations on surfaces and expected frequencies of hand-to-surface and orifice contacts and handwashing. Box plots and statistical hypothesis testing were used to compare the model-predicted and experimentally measured virus concentrations on nurses' hands. The model was validated with the experimental bacteriophage tracer data because the distribution for model-predicted virus concentrations on hands captured all observed value ranges, and interquartile ranges for model and experimental values overlapped for all comparison time points. Wilcoxon rank sum tests showed no significant differences in distributions of model-predicted and experimentally measured virus concentrations on hands. However, limitations in the tracer study indicate that more data are needed to instill more confidence in this validation. Next model development steps include addressing viral concentrations that would be found naturally in healthcare environments and measuring the risk reductions predicted for various infection control interventions.

Summary 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%.

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.

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% pImportanceInfection 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).

not available.

Current microbial exposure models assume that microbial exchange follows a concentration gradient during hand-to-surface contacts. Our objectives were to evaluate this assumption using transfer efficiency experiments and to evaluate a model's ability to explain concentration changes using approximate Bayesian computation (ABC) on these experimental data. Experiments were conducted with two phages (MS2, Φ X174) simultaneously to study bidirectional transfer. Concentrations on the fingertip and surface were quantified before and after fingertip-to-surface contacts. Prior distributions for surface and fingertip swabbing efficiencies and transfer efficiency were used to estimate concentrations on the fingertip and surface post contact. To inform posterior distributions, Euclidean distances were calculated for predicted detectable concentrations (log 10 PFU cm −2 ) on the fingertip and surface post contact in comparison with experimental values. To demonstrate the usefulness of posterior distributions in calibrated model applications, posterior transfer efficiencies were used to estimate rotavirus infection risks for a fingertip-to-surface and subsequent fingertip-to-mouth contact. Experimental findings supported the transfer gradient assumption. Through ABC, the model explained concentration changes more consistently when concentrations on the fingertip and surface were similar. Future studies evaluating microbial transfer should consider accounting for differing fingertip-to-surface and surface-to-fingertip transfer efficiencies and extend this work for other microbial types.

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.

Background Emergency medical service (EMS) vehicles may pose pathogen exposure risks for patients on route to a healthcare facility. One organism, particularly of concern in this environment, is methicillin-resistant Staphylococcus aureus (MRSA). The objective of this study was to develop a surface transmission model for estimating MRSA infection risk reductions associated with motion-activated ultraviolet (UV-C) light disinfection on surfaces in between hand-to-surface contacts in EMS vehicles. Methods A published mathematical transmission model was adapted to estimate MRSA transmission and infection risk for a patient during an emergency call. Two simulated scenarios were explored: 1) Baseline (no UV-C) and 2) UV-C disinfection (intervention) on a heart monitor, keyboard and portable radio in between contacts. The simulation was adapted to estimate the probability of a patient's wound exposure to MRSA and subsequent infection risk during a single transport. Variability and uncertainty in EMS personnel hand-to-medical equipment behavior and in MRSA concentration on high contact surfaces were accounted for within the model using a Monte Carlo simulation approach, where a series of potential outcomes could be considered. Results Over a 34-minute period, MRSA concentrations on both patient's wound and paramedics’ hands reached a steady state. Baseline infection (no UV-C light) risk was 3.5 × 10-8 on average. The infection risk for the UV-C light disinfection scenario was 2.53 × 10-8 on average. The reduction in mean infection risk was 27.8%. With the UV-C light intervention, there was a decrease in the overall MRSA surface concentrations within the EMS vehicle. Conclusions This mathematical simulation demonstrates that UV-C light disinfection within EMS vehicle may reduce MRSA infection risks for patients with when only three surfaces are targeted for treatment. Future studies are planned to better characterize paramedic behaviors that drive surface and hand contamination rates and to evaluate variables of UV-C light efficacy.

Background The purpose of this study was to relate experimentally measured log10 human norovirus reductions for a nonresidual (60% ethanol) and a residual (quaternary ammonium-based) hand sanitizer to infection risk reductions. Methods Human norovirus log10 reductions on hands for both sanitizers were experimentally measured using the ASTM International Standard E1838-10 method, with modification. Scenarios included product application to: (1) inoculated fingerpads with 30- and 60-second contact times, and (2) hands followed by inoculation with human norovirus immediately and 4 hours later. Hand sanitizer efficacies were used in a mathematical model estimating norovirus infection risk from a single hand-to-fomite contact under low and high environmental contamination conditions. Results The largest log10 reductions for the residual and nonresidual hand sanitizers were for a 60-second contact time, reducing infection risk by approximately 99% and 85%, respectively. Four hours after application, the residual hand sanitizer reduced infection risks by 78.5% under high contamination conditions, whereas the nonresidual hand sanitizer offered no reduction. Discussion Log10 virus and infection risk reductions were consistently greater for the residual hand sanitizer under all scenarios. Further data describing residual hand sanitizer efficacy with additional contamination or tactile events are needed. Conclusions Residual antinoroviral hand sanitizers may reduce infection risks for up to 4 hours.

To develop an exposure and risk assessment model to estimate listeriosis infection risks for Peruvian women.A simulation model was developed utilising Listeria monocytogenes concentrations on kitchen and latrine surfaces in Peruvian homes, hand trace data from Peruvian women and behavioural data from literature. Scenarios involving varying proportions of uncontaminated, or 'clean', surfaces and non-porous surfaces were simulated. Infection risks were estimated for 4, 6 and 8 h of behaviours and interactions with surfaces.Although infection risks were estimated across scenarios for various time points (e.g. 4, 6, 8 h), overall mean estimated infection risks for all scenarios were ≥ 0.31. Infection risks increased as the proportions of clean surfaces decreased. Hand-to-general surface contacts accounted for the most cumulative change in L. monocytogenes concentration on hands.In addition to gaining insights on how human behaviours affect exposure and infection risk, this model addressed uncertainties regarding the influence of household surface contamination levels. Understanding the influence of surface contamination in preventing pathogen transmission in households could help to develop intervention strategies to reduce L. monocytogenes infection and associated health risks.Développer un modèle d'exposition et d’évaluation des risques pour estimer les risques d'infection par la listériose chez les femmes péruviennes. MÉTHODES: Un modèle de simulation a été développé en utilisant des concentrations de Listeria monocytogenes sur la surface des cuisines et des latrines dans des foyers péruviens, des données de traces de mains de femmes péruviennes et des données comportementales de la littérature. Des scénarios impliquant différentes proportions de surfaces non contaminées ou «propres» et de surfaces non poreuses ont été simulés. Les risques d'infection ont été estimés pour 4, 6 et 8 heures de comportements et d'interactions avec les surfaces. RÉSULTATS: Bien que les risques d'infection aient été estimés pour tous les scénarios à différents moments (par ex. à 4, 6 ou 8 heures), les risques d'infection globaux moyens estimés pour tous les scénarios étaient ≥ 0,31. Les risques d'infection augmentaient à mesure que les proportions de surfaces propres diminuaient. Les contacts entre la main et les surfaces générales contribuent pour le plus de changement cumulatif de la concentration de L. monocytogenes sur les mains.En plus de comprendre comment les comportements humains affectent l'exposition et le risque d'infection, ce modèle a traité des incertitudes quant à l'influence des niveaux de contamination des surfaces ménagers. Comprendre l'influence de la contamination de surface dans la prévention de la transmission d'agents pathogènes dans les ménages pourrait aider à développer des stratégies d'intervention pour réduire l'infection à L. monocytogenes et les risques associés pour la santé.

Background Quantitative tools are needed to relate infection control interventions to infection risk reductions. Methods A model for predicting virus concentrations on hands was used to predict rotavirus, rhinovirus, and influenza A virus doses. Variability in behaviors, transfer efficiencies for various contact types, and surface areas of contact were included. Dose-response curves were used to relate estimated doses to infection risks. Percent reductions from baseline in average rotavirus, rhinovirus, and influenza A virus dose and infection risk were calculated for interventions. Results Baseline average infection risks for rotavirus, rhinovirus, and influenza A virus were 0.43, 0.20, and 5.51 × 10−6, respectively. One and 2 cleaning events decreased average viral infection risks by 6.98%-17.06% and 13.95%-34.66%, respectively. A 15% increase in hand compliance decreased average infection risks by 6.98%-20.51%. A 15% increase in hand hygiene compliance paired with 2 cleaning events decreased average infection risks by 20.93%-47.55%. Discussion This study demonstrates the infection risk benefits of combined interventions. Conclusions Models such as the one in this study could be used to optimize timing and frequency of cleaning events and to create hand hygiene compliance goals to achieve infection risk targets.

Irrigation water regulations use Escherichia coli as an indicator of fecal contamination. However, it is well documented that bacterial indicators may not be ideal for predicting viral pathogen presence. Viral pathogens survive relatively long periods in water, are resistant to treatment processes, and cause a large number of illnesses annually. Therefore, this study explored the application of quantitative microbial risk assessment (QMRA) to understand adenovirus and enterovirus concentration limits in irrigation water to achieve an acceptable annual risk estimate of 1 infection per 10,000 exposures. A two-step approach was used to define target viral concentrations: 1) a risk model describing infection from consuming irrigated leafy greens and 2) optimization of the risk model using Monte Carlo simulation to define viral concentrations with an output risk value of 1 × 10−4. Our QMRA estimates target concentrations for adenoviruses and enteroviruses would be 6.9 × 10−3 and 1.5 genome copies/100 mL irrigation water, respectively. Improving the accuracy of the risk model will require a more robust survey of enteric viruses in irrigation waters and dose-response models specific for adenoviruses and enteroviruses present throughout the farm-to-fork continuum.. This research contributes to the important step of adapting irrigation water monitoring practices to more accurately represent viral pathogens’ role in foodborne outbreaks.

Background: Wheelchairs can contribute to healthcare-associated infection transmission due to direct contact with patients and healthcare workers and due to wide spatial movement in facilities. Objective: We utilized location data of a wheelchair to inform an agent-based model for estimating the contribution of a single contaminated patient ride in a wheelchair to subsequent environmental contamination and to estimate the potential for wheelchair disinfection between patients to disrupt this spread. Methods: The destination and origin of wheelchairs were tracked in several facility locations: specialty care services, long-term care, radiology, acute care, common spaces, domiciliary, and outpatient clinics. An agent-based model was developed in which the probability of the wheelchair traveling directly from one location to another was informed by wheelchair origin and destination data. We assumed that the first patient’s hands were contaminated with methicillin-resistant Staphylococcus aureus (MRSA). For each patient trip, each simulated patient made contact with the wheelchair arm rests and a surface in the destination location. To evaluate potential exposures of uninfected patients, all patients riding in the wheelchair after the contaminated patient were assumed to be uncontaminated. In total, 50 patient rides were simulated. The concentration and number of contaminated surfaces in each hospital area were compared in addition to the average concentration of MRSA on patient hands over time. The intervention simulation involved a disinfection of wheelchair armrests with 90%, 70%, or 50% efficacy. Results: The 3 areas that had the largest estimated number of contaminated surfaces after 50 wheelchair trips following the first patient assumed to be infected were specialty care services, long-term care, and acute care. This finding was consistent with the paths that were most frequented by the wheelchair. Without cleaning between patients, the fiftieth patient to use the wheelchair had an average MRSA concentration of 41.5 CFU/cm2. With cleaning between patients, assuming a 50% cleaning efficacy, average MRSA concentration on the hands for the fiftieth patient was reduced to 7.4 ×10-14 CFU/cm2. Conclusions: We have demonstrated that cleaning, even with efficacies as low as 50%, may protect patients using contaminated wheelchairs from potential pathogen exposures. This study also demonstrates that tracking portable equipment can be useful not only for exposure modeling but also for predicting where the largest number of surfaces contaminated via portable equipment routes may be found. Future steps include performing a sensitivity analysis to evaluate the influence of spatial assumptions.Funding: NoneDisclosures: None

Background Contaminant transfer has predominantly been studied under single hand-to-fomite contact scenarios as opposed to more realistic conditions that include repetitive contacts. Our study objective was to evaluate the transfer of a fluorescent powder from finger to surfaces during sequential repetitive contacts whilst investigating the effect of wearing nitrile gloves and three different surface types: plastic, glass, cloth. Methods Eighty-three participants pressed the index finger of their dominant hand into fluorescent powder. Each participant then made five sequential contacts with a surface for six trials (3 surfaces, gloved and ungloved). Loading weights were recorded on a balance during each contact. Images of the transferred powder's fluorescence were captured for the five contact areas under ultra-violet light, and transferred powder surface area and fluorescence intensity, a proxy for the amount of transferred powder, were quantified with an image analysis software: Image J. Results Participants tended to contact fomites with greater force with gloves than without. The amount of powder transferred tended to decrease over multiple contacts despite a general increase in contact force. Greater loading weight was associated with a greater area of the transferred powder. Gloved contacts tended to transfer less powder to glass surfaces, while a similar amount of powder was transferred for gloved vs. ungloved contacts with cloth or plastic. The area of powder transferred was greater for ungloved contacts than for gloved contacts for those wearing size small gloves. More similar areas of powder were transferred for gloved vs ungloved contacts for those wearing size medium or large. Conclusions This study demonstrates that surface type, glove use, and even glove size may affect the potential for contamination of surfaces. Glove use generally decreased powder transfer, especially for glass surfaces. The next steps include exploring the effects of individual differences on these patterns and utilizing these data in healthcare fomite transmission risk assessments.

Data 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 × 10−2 from 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. IMPORTANCE This 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.

Norovirus accounts for a large portion of the gastroenteritis disease burden, and outbreaks have occurred in a wide variety of environments. Understanding the role of fomites in norovirus transmission will inform behavioral interventions, such as hand washing and surface disinfection. The purpose of this study was to estimate the contribution of fomite-mediated exposures to infection and illness risks in outbreaks. A simulation model in discrete time that accounted for hand-to-porous surfaces, hand-to-nonporous surfaces, hand-to-mouth, -eyes, -nose, and hand washing events was used to predict 17 hr of simulated human behavior. Norovirus concentrations originated from monitoring contamination levels on surfaces during an outbreak on houseboats. To predict infection risk, two dose-response models (fractional Poisson and

It is known that the use of EPA-registered surface disinfectant sprays can reduce infection risk if used according to the manufacturer's instructions. However, there are currently no standards for health care environments related to contamination levels on surfaces. The significance of this research is in quantifying needed reductions to meet various risk targets using realistic viral concentrations on surfaces for health care environments. This research informs the design of cleaning protocols by demonstrating that multiple applications may be needed to reduce risk and by highlighting a need for more models exploring the relationship among microbial contamination of surfaces, patient and health care worker behaviors, and infection risks., Nosocomial viral infections are an important cause of health care-acquired infections where fomites have a role in transmission. Using stochastic modeling to quantify the effects of surface disinfection practices on nosocomial pathogen exposures and infection risk can inform cleaning practices. The purpose of this study was to predict the effect of surface disinfection on viral infection risks and to determine needed viral reductions to achieve risk targets. Rotavirus, rhinovirus, and influenza A virus infection risks for two cases were modeled. Case 1 utilized a single fomite contact approach, while case 2 assumed 6 h of contact activities. A 94.1% viral reduction on surfaces and hands was measured following a single cleaning round using an Environmental Protection Agency (EPA)-registered disinfectant in an urgent care facility. This value was used to model the effect of a surface disinfection intervention on infection risk. Risk reductions for other surface-cleaning efficacies were also simulated. Surface reductions required to achieve risk probability targets were estimated. Under case 1 conditions, a 94.1% reduction in virus surface concentration reduced infection risks by 94.1%. Under case 2 conditions, a 94.1% reduction on surfaces resulted in median viral infection risks being reduced by 92.96 to 94.1% and an influenza A virus infection risk below one in a million. Surface concentration in the equations was highly correlated with dose and infection risk outputs. For rotavirus and rhinovirus, a >99.99% viral surface reduction would be needed to achieve a one-in-a-million risk target. This study quantifies reductions of infection risk relative to surface disinfectant use and demonstrates that risk targets for low-infectious-dose organisms may be more challenging to achieve. IMPORTANCE It is known that the use of EPA-registered surface disinfectant sprays can reduce infection risk if used according to the manufacturer's instructions. However, there are currently no standards for health care environments related to contamination levels on surfaces. The significance of this research is in quantifying needed reductions to meet various risk targets using realistic viral concentrations on surfaces for health care environments. This research informs the design of cleaning protocols by demonstrating that multiple applications may be needed to reduce risk and by highlighting a need for more models exploring the relationship among microbial contamination of surfaces, patient and health care worker behaviors, and infection risks.

Background Study objectives were to track the transfer of microbes on soft surfaces in health care environments and determine the efficiency of an Environmental Protection Agency (EPA)–registered soft surface sanitizer in the health care environment. Methods Soft surfaces at 3 health care facilities were sampled for heterotrophic plate count (HPC) bacteria, Staphylococcus spp, Streptococcus pyogenes , and Escherichia coli followed by a tracer study with a virus surrogate seeded onto volunteer hands and commonly touched surfaces. The occurrence of microbial contaminants was determined along with microbial reductions using the soft surface sanitizer. Soft surfaces were swabbed pre- and postintervention. Results Tracer viruses spread to 20%-64% and 13%-41% of surfaces in long-term health care facilities and physicians' offices, respectively. Only 1 pathogen, methicillin-resistant Staphylococcus aureus , was recovered. The waiting room chairs had the highest concentration of HPC bacteria before disinfection (145.4 ± 443.3 colony forming units [cfu]/cm 2 ), and the privacy curtains had the lowest (39.5 ± 84.2 cfu/cm 2 ). Reductions of up to 98.5% were achieved with the sanitizer in health care settings and up to 99.99% under controlled laboratory conditions. Conclusions Soft surfaces are involved in the spread of microbes throughout health care facilities. Routine application of an EPA-registered sanitizer for soft surfaces can help to reduce the microbial load and minimize exposure risks.

Converging evidence from human and animal studies suggests that decision-making relies upon a distributed neural network based in the frontal lobes. In particular, models of decision-making emphasize the involvement of orbitofrontal cortices (OFC) and the medial wall. While decision-making has been studied broadly as a class of executive function, recent models have suggested the differentiation between risky and ambiguous decision-making. Given recent emphasis on the role of OFC in affectively laden "hot" executive function and dorsolateral prefrontal cortex (DLPFC) in more purely cognitive "cool" executive function, we hypothesize that the neural substrates of decision-making may differ depending on the nature of the decision required. To test this hypothesis, we used recently developed meta-analytic techniques to examine the existent functional neuroimaging literature. An initial meta-analysis of decision-making, both risky and ambiguous, found significantly elevated probabilities of activation in frontal and parietal regions, thalamus, and caudate. Ambiguous decision-making was associated with activity in DLPFC, regions of dorsal and subcallosal anterior cingulate cortex (ACC), and parietal cortex. Risky decision-making was associated with activity in OFC, rostral portions of the ACC, and parietal cortex. Direct statistical comparisons revealed significant differences between risky and ambiguous decision-making in frontal regions, including OFC, DLPFC, and ACC, that were consistent with study hypotheses. These findings provide evidence for the dissociation of neural circuits underlying risky and ambiguous decision-making, reflecting differential involvement of affective "hot" and cognitive "cool" processes.