A 17-month longitudinal surface sampling study carried out on public transport vehicles operating in England during the COVID-19 pandemic identified low levels of SARS-CoV-2 RNA contamination.

AIMS: To monitor severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA contamination in vehicles operating in England during the pandemic, to better understand transmission risk of SARS-CoV-2 on public transport. METHODS AND RESULTS: We collected 1314 surface samples between December 2020 and April 2022 on trains and buses managed by five different transport operators. The presence of SARS-CoV-2 RNA was investigated through reverse transcription polymerase chain reaction (RT-PCR). SARS-CoV-2 RNA was found on 197 (15%) of the 1314 surfaces sampled, including seat head rests, handholds, and air extract grilles, but the levels of RNA recovered on those samples (median value of 23.4, interquartile range: 14.3-35.4, N gene copies per extraction) made the presence of infectious virus at the time of sampling extremely unlikely. However, detection rates varied over time with peaks broadly coinciding with times of high community transmission, when it was more likely that people infected with SARS-CoV-2 were travelling on public transport. CONCLUSION: During the pandemic, and as in other public spaces, low levels of SARS-CoV-2 RNA were found on surfaces associated with public transport.

The Wells-Riley model revisited: Randomness, heterogeneity, and transient behaviours.

The Wells-Riley model has been widely used to estimate airborne infection risk, typically from a deterministic point of view (i.e., focusing on the average number of infections) or in terms of a per capita probability of infection. Some of its main limitations relate to considering well-mixed air, steady-state concentration of pathogen in the air, a particular amount of time for the indoor interaction, and that all individuals are homogeneous and behave equally. Here, we revisit the Wells-Riley model, providing a mathematical formalism for its stochastic version, where the number of infected individuals follows a Binomial distribution. Then, we extend the Wells-Riley methodology to consider transient behaviours, randomness, and population heterogeneity. In particular, we provide analytical solutions for the number of infections and the per capita probability of infection when: (i) susceptible individuals remain in the room after the infector leaves, (ii) the duration of the indoor interaction is random/unknown, and (iii) infectors have heterogeneous quanta production rates (or the quanta production rate of the infector is random/unknown). We illustrate the applicability of our new formulations through two case studies: infection risk due to an infectious healthcare worker (HCW) visiting a patient, and exposure during lunch for uncertain meal times in different dining settings. Our results highlight that infection risk to a susceptible who remains in the space after the infector leaves can be nonnegligible, and highlight the importance of incorporating uncertainty in the duration of the indoor interaction and the infectivity of the infector when estimating risk.

Modeling the effect of temperature and relative humidity on exposure to SARS-CoV-2 in a mechanically ventilated room.

Computational fluid dynamics models have been developed to predict airborne exposure to the SARS-CoV-2 virus from a coughing person in a mechanically ventilated room. The models were run with three typical indoor air temperatures and relative humidities (RH). Quantile regression was used to indicate whether these have a statistically significant effect on the airborne exposure. Results suggest that evaporation is an important effect. Evaporation leads to respiratory particles, particularly those with initial diameters between 20 and 100 µm, remaining airborne for longer, traveling extended distances and carrying more viruses than expected from their final diameter. In a mechanically ventilated room, with all of the associated complex air movement and turbulence, increasing the RH may result in reduced airborne exposure. However, this effect may be so small that other factors, such as a small change in proximity to the infected person, could rapidly counter the effect. The effect of temperature on the exposure was more complex, with both positive and negative correlations. Therefore, within the range of conditions studied here, there is no clear guidance on how the temperature should be controlled to reduce exposure. The results highlight the importance of ventilation, face coverings and maintaining social distancing for reducing exposure.

The impact of ventilation rate on reducing the microorganisms load in the air and on surfaces in a room-sized chamber.

Hospital-acquired infections (HAIs) are a global challenge incurring mortalities and high treatment costs. The environment plays an important role in transmission due to contaminated air and surfaces. This includes microorganisms' deposition from the air onto surfaces. Quantifying the deposition rate of microorganisms enables understanding surface contamination and can inform strategies to mitigate the infection risk. We developed and validated a novel Automated Multiplate Passive Air Sampling (AMPAS) device. This enables sequences of passive deposition samples to be collected over a controlled time period without human intervention. AMPAS was used with air sampling to measure the effect of ventilation rate and spatial location on the deposition rate of aerosolized Staphylococcus aureus in a 32 m3 chamber. Increasing the ventilation rate from 3 to 6 ACH results in a reduction of microbial load in the air and on surfaces by 45% ± 10% and 44% ± 32%, respectively. The deposition rate onto internal surfaces λd was calculated as 1.38 ± 0.48 h-1 . Samples of airborne and surface microorganisms taken closer to the ventilation supply showed a lower concentration than close to the extract. The findings support the importance of controlling the ventilation and the environmental parameters to mitigate both air and surface infection risks in the hospital environment.

Effect of Relative Humidity on Transfer of Aerosol-Deposited Artificial and Human Saliva from Surfaces to Artificial Finger-Pads.

Surface to hand transfer of viruses represents a potential mechanism for human exposure. An experimental process for evaluating the touch transfer of aerosol-deposited material is described based on controlling surface, tribological, and soft matter components of the transfer process. A range of high-touch surfaces were evaluated. Under standardized touch parameters (15 N, 1 s), relative humidity (RH) of the atmosphere around the contact transfer event significantly influenced transfer of material to the finger-pad. At RH < 40%, transfer from all surfaces was <10%. Transfer efficiency increased markedly as RH increased, reaching a maximum of approximately 50%. The quantity of material transferred at specific RHs above 40% was also dependent on roughness of the surface material and the properties of the aerosol-deposited material. Smooth surfaces, such as melamine and stainless steel, generated higher transfer efficiencies compared to those with textured roughness, such as ABS pinseal and KYDEX® plastics. Pooled human saliva was transferred at a lower rate compared to artificial saliva, indicating the role of rheological properties. The artificial saliva data were modeled by non-linear regression and the impact of environmental humidity and temperature were evaluated within a Quantitative Microbial Risk Assessment model using SARS-CoV-2 as an example. This illustrated that the trade-off between transfer efficiency and virus survival may lead to the highest risks of fomite transmissions in indoor environments with higher humidity.

Modeling and experimental study of dispersion and deposition of respiratory emissions with implications for disease transmission.

The ability to model the dispersion of pathogens in exhaled breath is important for characterizing transmission of the SARS-CoV-2 virus and other respiratory pathogens. A Computational Fluid Dynamics (CFD) model of droplet and aerosol emission during exhalations has been developed and for the first time compared directly with experimental data for the dispersion of respiratory and oral bacteria from ten subjects coughing, speaking, and singing in a small unventilated room. The modeled exhalations consist of a warm, humid, gaseous carrier flow and droplets represented by a discrete Lagrangian particle phase which incorporates saliva composition. The simulations and experiments both showed greater deposition of bacteria within 1 m of the subject, and the potential for a substantial number of bacteria to remain airborne, with no clear difference in airborne concentration of small bioaerosols (<10 µm diameter) between 1 and 2 m. The agreement between the model and the experimental data for bacterial deposition directly in front of the subjects was encouraging given the uncertainties in model input parameters and the inherent variability within and between subjects. The ability to predict airborne microbial dispersion and deposition gives confidence in the ability to model the consequences of an exhalation and hence the airborne transmission of respiratory pathogens such as SARS-CoV-2.

Modeling the factors that influence exposure to SARS-CoV-2 on a subway train carriage.

We propose the Transmission of Virus in Carriages (TVC) model, a computational model which simulates the potential exposure to SARS-CoV-2 for passengers traveling in a subway rail system train. This model considers exposure through three different routes: fomites via contact with contaminated surfaces; close-range exposure, which accounts for aerosol and droplet transmission within 2 m of the infectious source; and airborne exposure via small aerosols which does not rely on being within 2 m distance from the infectious source. Simulations are based on typical subway parameters and the aim of the study is to consider the relative effect of environmental and behavioral factors including prevalence of the virus in the population, number of people traveling, ventilation rate, and mask wearing as well as the effect of model assumptions such as emission rates. Results simulate generally low exposures in most of the scenarios considered, especially under low virus prevalence. Social distancing through reduced loading and high mask-wearing adherence is predicted to have a noticeable effect on reducing exposure through all routes. The highest predicted doses happen through close-range exposure, while the fomite route cannot be neglected; exposure through both routes relies on infrequent events involving relatively few individuals. Simulated exposure through the airborne route is more homogeneous across passengers, but is generally lower due to the typically short duration of the trips, mask wearing, and the high ventilation rate within the carriage. The infection risk resulting from exposure is challenging to estimate as it will be influenced by factors such as virus variant and vaccination rates.

Modeling fomite-mediated SARS-CoV-2 exposure through personal protective equipment doffing in a hospital environment.

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.

Use of Multiple Low Cost Carbon Dioxide Sensors to Measure Exhaled Breath Distribution with Face Mask Type and Wearing Behaviour.

The use of cloth face coverings and face masks has become widespread in light of the COVID-19 pandemic. This paper presents a method of using low cost wirelessly connected carbon dioxide (CO2) sensors to measure the effects of properly and improperly worn face masks on the concentration distribution of exhaled breath around the face. Four types of face masks are used in two indoor environment scenarios. CO2 as a proxy for exhaled breath is being measured with the Sensirion SCD30 CO2 sensor, and data are being transferred wirelessly to a base station. The exhaled CO2 is measured in four directions at various distances from the head of the subject, and interpolated to create spatial heat maps of CO2 concentration. Statistical analysis using the Friedman's analysis of variance (ANOVA) test is carried out to determine the validity of the null hypotheses (i.e., distribution of the CO2 is same) between different experiment conditions. Results suggest CO2 concentrations vary little with the type of mask used; however, improper use of the face mask results in statistically different CO2 spatial distribution of concentration. The use of low cost sensors with a visual interpolation tool could provide an effective method of demonstrating the importance of proper mask wearing to the public.

Respirators, face masks, and their risk reductions via multiple transmission routes for first responders within an ambulance.

First responders may have high SARS-CoV-2 infection risks due to working with potentially infected patients in enclosed spaces. The study objective was to estimate infection risks per transport for first responders and quantify how first responder use of N95 respirators and patient use of cloth masks can reduce these risks. A model was developed for two Scenarios: an ambulance transport with a patient actively emitting a virus in small aerosols that could lead to airborne transmission (Scenario 1) and a subsequent transport with the same respirator or mask use conditions, an uninfected patient; and remaining airborne SARS-CoV-2 and contaminated surfaces due to aerosol deposition from the previous transport (Scenario 2). A compartmental Monte Carlo simulation model was used to estimate the dispersion and deposition of SARS-CoV-2 and subsequent infection risks for first responders, accounting for variability and uncertainty in input parameters (i.e., transport duration, transfer efficiencies, SARS-CoV-2 emission rates from infected patients, etc.). Infection risk distributions and changes in concentration on hands and surfaces over time were estimated across sub-Scenarios of first responder respirator use and patient cloth mask use. For Scenario 1, predicted mean infection risks were reduced by 69%, 48%, and 85% from a baseline risk (no respirators or face masks used) of 2.9 × 10-2 ± 3.4 × 10-2 when simulated first responders wore respirators, the patient wore a cloth mask, and when first responders and the patient wore respirators or a cloth mask, respectively. For Scenario 2, infection risk reductions for these same Scenarios were 69%, 50%, and 85%, respectively (baseline risk of 7.2 × 10-3 ± 1.0 × 10-2). While aerosol transmission routes contributed more to viral dose in Scenario 1, our simulations demonstrate the ability of face masks worn by patients to additionally reduce surface transmission by reducing viral deposition on surfaces. Based on these simulations, we recommend the patient wear a face mask and first responders wear respirators, when possible, and disinfection should prioritize high use equipment.

What is the relationship between indoor air quality parameters and airborne microorganisms in hospital environments? A systematic review and meta-analysis.

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 Ë‚ 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.

Effects of patient room layout on viral accruement on healthcare professionals' hands.

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.

Seasonal variation in airborne infection risk in schools due to changes in ventilation inferred from monitored carbon dioxide.

The year 2020 has seen the world gripped by the effects of the COVID-19 pandemic. It is not the first time, nor will it be last, that our increasingly globalized world has been significantly affected by the emergence of a new disease. In much of the Northern Hemisphere, the academic year begins in September, and for many countries, September 2020 marked the return to full schooling after some period of enforced closure due to COVID-19. In this paper, we focus on the airborne spread of disease and investigate the likelihood of transmission in school environments. It is crucial to understand the risk airborne infection from COVID-19 might pose to pupils, teachers, and their wider social groups. We use monitored CO2 data from 45 classrooms in 11 different schools from within the UK to estimate the likelihood of infection occurring within classrooms regularly attended by the same staff and pupils. We determine estimates of the number of secondary infections arising via the airborne route over pre/asymptomatic periods on a rolling basis. Results show that, assuming relatively quiet desk-based work, the number of secondary infections is likely to remain reassuringly below unity; however, it can vary widely between classrooms of the same school even when the same ventilation system is present. Crucially, the data highlight significant variation with the seasons with January being nearly twice as risky as July. We show that such seasonal variations in risk due to changes in ventilation rates are robust and our results hold for wide variations in disease parameterizations, suggesting our results may be applied to a number of different airborne diseases.

Modelling uncertainty in the relative risk of exposure to the SARS-CoV-2 virus by airborne aerosol transmission in well mixed indoor air.

We present a mathematical model and a statistical framework to estimate uncertainty in the number of SARS-CoV-2 genome copies deposited in the respiratory tract of a susceptible person, ∑ n , over time in a well mixed indoor space. By relating the predicted median ∑ n for a reference scenario to other locations, a Relative Exposure Index (REI) is established that reduces the need to understand the infection dose probability but is nevertheless a function of space volume, viral emission rate, exposure time, occupant respiratory activity, and room ventilation. A 7  h day in a UK school classroom is used as a reference scenario because its geometry, building services, and occupancy have uniformity and are regulated. The REI is used to highlight types of indoor space, respiratory activity, ventilation provision and other factors that increase the likelihood of far field ( > 2  m) exposure. The classroom reference scenario and an 8  h day in a 20 person office both have an REI ≃ 1 and so are a suitable for comparison with other scenarios. A poorly ventilated classroom (1.2 l s-1 per person) has REI > 2 suggesting that ventilation should be monitored in classrooms to minimise far field aerosol exposure risk. Scenarios involving high aerobic activities or singing have REI > 1 ; a 1  h gym visit has a median REI = 1 . 4 , and the Skagit Choir superspreading event has REI > 12 . Spaces with occupancy activities and exposure times comparable to those of the reference scenario must preserve the reference scenario volume flow rate as a minimum rate to achieve REI = 1 , irrespective of the number of occupants present.

Minimal transmission in an influenza A (H3N2) human challenge-transmission model within a controlled exposure environment.

Uncertainty about the importance of influenza transmission by airborne droplet nuclei generates controversy for infection control. Human challenge-transmission studies have been supported as the most promising approach to fill this knowledge gap. Healthy, seronegative volunteer 'Donors' (n = 52) were randomly selected for intranasal challenge with influenza A/Wisconsin/67/2005 (H3N2). 'Recipients' randomized to Intervention (IR, n = 40) or Control (CR, n = 35) groups were exposed to Donors for four days. IRs wore face shields and hand sanitized frequently to limit large droplet and contact transmission. One transmitted infection was confirmed by serology in a CR, yielding a secondary attack rate of 2.9% among CR, 0% in IR (p = 0.47 for group difference), and 1.3% overall, significantly less than 16% (p<0.001) expected based on a proof-of-concept study secondary attack rate and considering that there were twice as many Donors and days of exposure. The main difference between these studies was mechanical building ventilation in the follow-on study, suggesting a possible role for aerosols.

Quantitative aerobiologic analysis of an influenza human challenge-transmission trial.

Despite evidence that airborne transmission contributes to influenza epidemics, limited knowledge of the infectiousness of human influenza cases hinders pandemic preparedness. We used airborne viral source strength and indoor CO2 monitoring from the largest human influenza challenge-transmission trial (EMIT: Evaluating Modes of Influenza Transmission, ClinicalTrials.gov number NCT01710111) to compute an airborne infectious dose generation rate q = 0.11 (95% CI 0.088, 0.12)/h and calculate the quantity of airborne virus per infectious dose σ = 1.4E + 5 RNA copies/quantum (95% CI 9.9E + 4, 1.8E + 5). We then compared these calculated values to available data on influenza airborne infectious dose from several previous studies, and applied the values to dormitory room environments to predict probability of transmission between roommates. Transmission risk from typical, moderately to severely symptomatic influenza cases is dramatically decreased by exposure reduction via increasing indoor air ventilation. The minority of cases who shed the most virus (ie, supershedders) may pose great risk even in well-ventilated spaces. Our modeling method and estimated infectiousness provide a ground work for (a) epidemiologic studies of transmission in non-experimental settings and (b) evaluation of the extent to which airborne exposure control strategies could limit transmission risk.

Evaluating a transfer gradient assumption in a fomite-mediated microbial transmission model using an experimental and Bayesian approach.

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, ΦX174) 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 (log10 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 transfer to fingertips during sequential surface contacts with and without gloves.

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.

A Multicompartment SIS Stochastic Model with Zonal Ventilation for the Spread of Nosocomial Infections: Detection, Outbreak Management, and Infection Control.

In this work, we study the environmental and operational factors that influence airborne transmission of nosocomial infections. We link a deterministic zonal ventilation model for the airborne distribution of infectious material in a hospital ward, with a Markovian multicompartment SIS model for the infection of individuals within this ward, in order to conduct a parametric study on ventilation rates and their effect on the epidemic dynamics. Our stochastic model includes arrival and discharge of patients, as well as the detection of the outbreak by screening events or due to symptoms being shown by infective patients. For each ventilation setting, we measure the infectious potential of a nosocomial outbreak in the hospital ward by means of a summary statistic: the number of infections occurred within the hospital ward until end or declaration of the outbreak. We analytically compute the distribution of this summary statistic, and carry out local and global sensitivity analysis in order to identify the particular characteristics of each ventilation regime with the largest impact on the epidemic spread. Our results show that ward ventilation can have a significant impact on the infection spread, especially under slow detection scenarios or in overoccupied wards, and that decreasing the infection risk for the whole hospital ward might increase the risk in specific areas of the health-care facility. Moreover, the location of the initial infective individual and the protocol in place for outbreak declaration both form an interplay with ventilation of the ward.

Aerosol-Transmitted Infections-a New Consideration for Public Health and Infection Control Teams.

Since the emergence of the 2003 severe acute respiratory syndrome (SARS), the 2003 reemergence of avian A/H5N1, the emergence of the 2009 pandemic influenza A/H1N1, the 2012 emergence of Middle East respiratory syndrome (MERS), the 2013 emergence of avian A/H7N9 and the 2014 Ebola virus outbreaks, the potential for the aerosol transmission of infectious agents is now routinely considered in the investigation of any outbreak. Although many organisms have traditionally been considered to be transmitted by only one route (e.g. direct/indirect contact and/or faecal-orally), it is now apparent that the aerosol transmission route is also possible and opportunistic, depending on any potentially aerosol-generating procedures, the severity of illness and the degree and duration of pathogen-shedding in the infected patient, as well as the environment in which these activities are conducted.This article reviews the evidence and characteristics of some of the accepted (tuberculosis, measles, chickenpox, whooping cough) and some of the more opportunistic (influenza, Clostridium difficile, norovirus) aerosol-transmitted infectious agents and outlines methods of detecting and quantifying transmission.