Impact of terminal cleaning in rooms previously occupied by patients with healthcare-associated infections.

Healthcare associated infections (HAIs) are costly but preventable. A limited understanding of the effects of environmental cleaning on the riskiest HAI associated pathogens is a current challenge in HAI prevention. This project aimed to quantify the effects of terminal hospital cleaning practices on HAI pathogens via environmental sampling in three hospitals located throughout the United States. Surfaces were swabbed from 36 occupied patient rooms with a laboratory-confirmed, hospital- or community-acquired infection of at least one of the four pathogens of interest (i.e., Acinetobacter baumannii (A. baumannii), methicillin resistant Staphylococcus aureus (MRSA), vancomycin resistant Enterococcus faecalis/faecium (VRE), and Clostridioides difficile (C. difficile)). Six nonporous, high touch surfaces (i.e., chair handrail, bed handrail, nurse call button, desk surface, bathroom counter near the sink, and a grab bar near the toilet) were sampled in each room for Adenosine Triphosphate (ATP) and the four pathogens of interest before and after terminal cleaning. The four pathogens of interest were detected on surfaces before and after terminal cleaning, but their levels were generally reduced. Overall, C. difficile was confirmed on the desk (n = 2), while MRSA (n = 24) and VRE (n = 25) were confirmed on all surface types before terminal cleaning. After cleaning, only MRSA (n = 6) on bed handrail, chair handrail, and nurse call button and VRE (n = 5) on bathroom sink, bed handrail, nurse call button, toilet grab bar, and C. difficile (n = 1) were confirmed. At 2 of the 3 hospitals, pathogens were generally reduced by >99% during terminal cleaning. One hospital showed that VRE increased after terminal cleaning, MRSA was reduced by 73% on the nurse call button, and VRE was reduced by only 50% on the bathroom sink. ATP detections did not correlate with any pathogen concentration. This study highlights the importance of terminal cleaning and indicates room for improvement in cleaning practices to reduce surface contamination throughout hospital rooms.

Development and evaluation of an intensive short course: the Quantitative Microbial Risk Assessment Interdisciplinary Instructional Institute (QMRA III).

Quantitative microbial risk assessment (QMRA) is a growing interdisciplinary field addressing exposures to microbial pathogens and infectious disease processes. Risk science is inherently interdisciplinary, but few of the contributing disciplinary programs offer courses and training specifically in QMRA. To develop multidisciplinary training in QMRA, an annual 10-day long intensive workshop was conducted from 2015 to 2019-the Quantitative Microbial Risk Assessment Interdisciplinary Instructional Institute (QMRA III). National leaders in the fields of public health, engineering, microbiology, epidemiology, communications, public policy, and QMRA served as instructors and mentors over the course of the program. To provide cross-training, multidisciplinary teams of 5-6 trainees were created from the approximately 30 trainees each year. A formal assessment of the program was performed based on observations and surveys containing Likert-type scales and open-ended prompts. In addition, a longitudinal alumni survey was also disseminated to facilitate the future redevelopment of QMRA institutes and determine the impact of the program. Across all years, trainees experienced statistically significant increases (P < 0.05) in their perceptions of their QMRA abilities (e.g., use of specific computer programs) and knowledge of QMRA constructs (e.g., risk management). In addition, 12 publications, three conference presentations, and two research grants were derived from the QMRA III institute projects or tangential research. The success of QMRA III indicates that a short course format can effectively address many multidisciplinary training needs. Key features of QMRA III, including the inter-disciplinary training approach, hands-on exercises, real-world institute projects, and interaction through a mentoring process, were vital for training multidisciplinary teams housing multiple forms of expertise. Future QMRA institutes are being redeveloped to leverage hybrid learning formats that can further the multidisciplinary training and mentoring objectives.

Projecting COVID-19 cases and hospital burden in Ohio.

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Fluence-based QMRA model for bacterial photorepair and regrowth in drinking water after decentralized UV disinfection.

Ultraviolet disinfection is a promising solution for decentralized drinking water systems such as communal water taps. A potential health risk is enzymatic photorepair of pathogens after UV disinfection, which can result in regrowth of pathogens. Even though photorepair is a known issue, no formal risk assessments have been conducted for photorepair after UV disinfection in drinking water. The main objective was to construct a quantitative microbial risk assessment (QMRA) of photorepair after UV disinfection of drinking water in a decentralized system. UV disinfection and photorepair kinetics for E. coli were modelled using reproducible fluence-based determinations. Impacts of water collection patterns, and wavelength-dependent water container material transmittance, sunlight intensity, and photorepair enzyme absorbance were quantified. After UV disinfection by 16 or 40 mJ/cm2 of < 5-log microorganisms per L, risk of infection did not exceed 1-in-10,000 under conditions permitting E. coli photorepair. Risk from photorepair was less than 1-in-10,000 for photorepair light exposure < 0.75 h throughout the day for UV fluence 16 mJ/cm2 or greater. UV disinfection followed by solar disinfection surpassing photoreactivation during storage reduced risk below 1-in-10,000 for photorepair light exposure > 2.5 h between modelled times of 9 AM - 3 PM. The model can be expanded to other pathogens as UV fluence and photorepair fluence response kinetics become available, and this QMRA can be used to inform the placement of community water access points to reduce risk of photorepair and ensure adequate shelf life of UV disinfected water under safe storage conditions.

Wastewater surveillance of SARS-CoV-2 in dormitories as a part of comprehensive university campus COVID-19 monitoring.

Wastewater-based epidemiology is an effective tool for monitoring infectious disease spread or illicit drug use within communities. At the Ohio State University, we conducted a SARS-CoV-2 wastewater surveillance program in the 2020-2021 academic year and compared results with the university-required weekly COVID-19 saliva testing to monitor COVID-19 infection prevalence in the on-campus residential communities. The objectives of the study were to rapidly track trends in the wastewater SARS-CoV-2 gene concentrations, analyze the relationship between case numbers and wastewater signals when adjusted using human fecal viral indicator concentrations (PMMoV, crAssphage) in wastewater, and investigate the relationship of the SARS-CoV-2 gene concentrations with wastewater parameters. SARS-CoV-2 nucleocapsid and envelope (N1, N2, and E) gene concentrations, determined with reverse transcription droplet digital PCR, were used to track SARS-CoV-2 viral loads in dormitory wastewater once a week at 6 sampling sites across the campus during the fall semester in 2020. During the following spring semester, research was focused on SARS-CoV2 N2 gene concentrations at 5 sites sampled twice a week. Spearman correlations both with and without adjusting using human fecal viral indicators showed a significant correlation (p < 0.05) between human COVID-19 positive case counts and wastewater SARS-CoV-2 gene concentrations. Spearman correlations showed significant relationships between N1 gene concentrations and both TSS and turbidity, and between E gene concentrations and both pH and turbidity. These results suggest that wastewater signal increases with the census of infected individuals, in which the majority are asymptomatic, with a statistically significant (p-value <0.05) temporal correlation. The study design can be utilized as a platform for rapid trend tracking of SARS-CoV-2 variants and other diseases circulating in various communities.

Knowledge to Predict Pathogens: Legionella pneumophila Lifecycle Systematic Review Part II Growth within and Egress from a Host Cell.

Legionella pneumophila (L. pneumophila) is a pathogenic bacterium of increasing concern, due to its ability to cause a severe pneumonia, Legionnaires' Disease (LD), and the challenges in controlling the bacteria within premise plumbing systems. L. pneumophila can thrive within the biofilm of premise plumbing systems, utilizing protozoan hosts for protection from environmental stressors and to increase its growth rate, which increases the bacteria's infectivity to human host cells. Typical disinfectant techniques have proven to be inadequate in controlling L. pneumophila in the premise plumbing system, exposing users to LD risks. As the bacteria have limited infectivity to human macrophages without replicating within a host protozoan cell, the replication within, and egress from, a protozoan host cell is an integral part of the bacteria's lifecycle. While there is a great deal of information regarding how L. pneumophila interacts with protozoa, the ability to use this data in a model to attempt to predict a concentration of L. pneumophila in a water system is not known. This systematic review summarizes the information in the literature regarding L. pneumophila's growth within and egress from the host cell, summarizes the genes which affect these processes, and calculates how oxidative stress can downregulate those genes.

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.

Bystander chemical exposures and injuries associated with nearby plastic sewer pipe manufacture: public health practice and lessons.

Cured-in-place pipes (CIPPs) are plastic liners manufactured inside existing damaged sanitary sewer, storm sewer, and water pipes that extend the service life of host pipes. This process often is conducted in neighborhoods and near roadways. Before, during, and after plastic manufacture, waste materials that include volatile materials are released into the air. Emissions from this manufacturing process can affect outdoor air quality and indoor air quality for buildings connected to the sewer system. We identified key issues and solicited stakeholder feedback to estimate and manage public health risks of CIPP-generated chemical air pollution. A work group representing 13 U.S. agencies and public health associations provided feedback and prioritized public health issues for action. To mitigate potential public and occupational health risks, additional testing and public health educational efforts were recommended. An improved understanding of CIPP chemical exposure pathways, as well as stakeholder needs and interests, is essential.

Comparing approaches for modelling indirect contact transmission of infectious diseases.

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.

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.

Development of methods to estimate microcystins removal and water treatment resiliency using mechanistic risk modelling.

Drinking water treatment processes are capable of removing microcystins but consistent operation of processes optimized for cyanobacterial harmful algal bloom (cHAB) conditions is not fiscally feasible. Therefore, utilities must ready themselves and start the cHAB processes as a reactionary response. Predictive analytics and modelling are impactful tools to prepare water systems for cHABs, but are still in early stages of development. Until those prospective models are completed, a method to determine best actions in advance of a bloom event thus improving system resiliency is needed. In this study, an adaptation of the quantitative microbial risk analysis (QMRA) methodology was applied to develop this method. This method and resulting models were developed around the Toledo (Ohio, USA) water crisis of 2014, but being mechanistic, they are easily adaptable to other systems' process operations data. Results from this internally validated model demonstrate how rapid action using both powdered activated carbon and measured increases in chlorine dose can mitigate health risks. Our research also demonstrates the importance of modelling the cellular status of the toxins (toxins either in an intact cell or in the water from a lysed cell). Risks were characterized using hazard quotients (HQ) and at the peak of the crisis ranged from a minimum of 0.00244 to a maximum of 2.84 for adults. In simulations where cHAB-specific treatment was used this decreased to 0.00057 and 0.236 respectively. We further outline how this methodology can be used to simulate water system resiliency to likely and aberrant microbial hazard events to plan for the best interventions to protect public health. This method can be used for other hazards expected to be variable in the future, where system prepatory planning is critical to continued public health protection. Considering the water quantity and quality fluctuations occurring and likely to intensify under climate change, this type of computationally supported preparedness is vital to maintaining robust water system resiliency.

Modeling COVID-19 infection risks for a single hand-to-fomite scenario and potential risk reductions offered by surface disinfection.

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.

A Data Simulation Method to Optimize a Mechanistic Dose-Response Model for Viral Loads of Hepatitis A.

Driven by the quantitative estimate of risk via the dose-response models, quantitative microbial risk assessment has been used successfully for public health interventions. The dose-response models are derived starting from an average exposed dose of infectious particles, this dictates the of dose data units required. Then dose-response data from animal model experiments are used to optimize these mechanistic dose-response models. For hepatitis A (Hep-A), the only available dose-response data use grams of feces for dose units. Therefore, to develop a dose-response model for Hep-A a method of converting these doses in grams of feces into infectious particles, while accounting for the uncertainty of this conversion is needed. This research develops a method to couple data simulation with the likelihood estimation method for model optimization to accomplish this. This adapted method uses data simulation to model the doses as viral particles while accounting for the within-group variability of this simulation. Then these simulated doses, coupled with the original dose-response data, are used to optimize the mechanistic dose-response models. This method results in a more computationally rigorous means of modeling these types of dose-response data. The resulting dose-response model for Hep-A is also more appropriate to use than the current option for Hep-A risk models.

COVID-19 surveillance in Southeastern Virginia using wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has been used to analyze markers in wastewater treatment plant (WWTP) influent to characterize emerging chemicals, drug use patterns, or disease spread within communities. This approach can be particularly helpful in understanding outbreaks of disease like the novel Coronavirus disease-19 (COVID-19) when combined with clinical datasets. In this study, three RT-ddPCR assays (N1, N2, N3) were used to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in weekly samples from nine WWTPs in southeastern Virginia. In the first several weeks of sampling, SARS-CoV-2 detections were sporadic. Frequency of detections and overall concentrations of RNA within samples increased from mid March into late July. During the twenty-one week study, SARS-CoV-2 concentrations ranged from 101 to 104 copies 100 mL-1 in samples where viral RNA was detected. Fluctuations in population normalized loading rates in several of the WWTP service areas agreed with known outbreaks during the study. Here we propose several ways that data can be presented spatially and temporally to be of greatest use to public health officials. As the COVID-19 pandemic wanes, it is likely that communities will see increased incidence of small, localized outbreaks. In these instances, WBE could be used as a pre-screening tool to better target clinical testing needs in communities with limited resources.

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.

Legionella pneumophila as a Health Hazard to Miners: A Pilot Study of Water Quality and QMRA.

Legionella pneumophila (L. pneumophila), the causative agent of legionellosis, is an aquatic bacterium that grows in warm water. Humans are only presented with a health risk when aerosolized water containing L. pneumophila is inhaled. In mining operations, aerosolized water is used as dust control and as part of the drilling operations, a currently ignored exposure route. This study characterized L. pneumophila concentrations in the mine's non-potable water and the relationship between L. pneumophila and chlorine concentrations. These concentrations informed a quantitative microbial risk assessment (QMRA) model to estimate the infection risk to miners exposed to aerosolized water containing L. pneumophila. Fourteen water samples were collected from seven locations at a mine and analyzed for temperature, pH, chlorine, and L. pneumophila serogroup. Most samples (93%) tested positive for L. pneumophila cells. The faucet from the sprinkler system on the adit level (entrance to the underground mine levels) showed the highest concentration of L. pneumophila (8.35 × 104 MPN/L). Disability adjusted life years (DALYs) were estimated in the QMRA model and showed that the risk for all miners was significantly lower (p < 0.0001) with the ventilation system on than when the system was off. Our study showed that the use of a ventilation system at the adit level of the mine reduced the risk of infection with aerosolized L. pneumophila.

Development of a dose-response model for Naegleria fowleri.

This study develops novel dose-response models for Naegleria fowleri from selected peer-reviewed experiments on the virulence based on the intranasal exposure pathway. One data set measured the response of mice intranasally inoculated with the amebae and the other study addressed the response of mice swimming in N. fowleri infected water. The measured response for both studies was death. All experimental data were best fit by the beta-Poisson dose-response model. The three swimming experiments could be pooled, and this is the final recommended model with an LD50 of 13,257 amebae. The results of this study provide a better estimate of the probability of the risk to N. fowleri exposure than the previous models developed based on an intravenous exposure. An accurate dose-response model is the first step in quantifying the risk of free-living amebae like N. fowleri, which pose risks in recreational environments and have been detected in drinking water and premise plumbing systems. A better understanding of this risk will allow for risk management that limits the ability for pathogen growth, proliferation, and exposure.

Modeling the role of fomites in a norovirus outbreak.

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 2F1 hypergeometric) were used to capture a range of infection risks. A triangular distribution describing the conditional probability of illness given an infection was multiplied by modeled infection risks to estimate illness risks. Infection risks ranged from 70.22% to 72.20% and illness risks ranged from 21.29% to 70.36%. A sensitivity analysis revealed that the number of hand-to-mouth contacts and the number of hand washing events had strong relationships with model-predicted doses. Predicted illness risks overlapped with leisure setting and environmental attack rates reported in the literature. In the outbreak associated with the viral concentrations used in this study, attack rates ranged from 50% to 86%. This model suggests that fomites may have accounted for 25% to 82% of illnesses in this outbreak. Fomite-mediated exposures may contribute to a large portion of total attack rates in outbreaks involving multiple transmission modes. The findings of this study reinforce the importance of frequent fomite cleaning and hand washing, especially when ill persons are present.

Salmonella risks due to consumption of aquaculture-produced shrimp.

The use of aquaculture is increasing to meet the growing global demand for seafood. However, the use of aquaculture for seafood production incurs potential human health risks, especially from enteric bacteria such as Salmonella spp. Salmonella spp. was the most frequently reported cause of outbreaks associated with crustaceans from 1998 to 2004. Among crustacean species, shrimp are the most economically important, internationally traded seafood commodity, and the most commonly aquaculture-raised seafood imported to the United States. To inform safe aquaculture practices, a quantitative microbial risk assessment (QMRA) was performed for wastewater-fed aquaculture, incorporating stochastic variability in shrimp growth, processing, and consumer preparation. Several scenarios including gamma irradiation, proper cooking, and improper cooking were considered in order to examine the relative importance of these practices in terms of their impact on risk. Median annual infection risks for all scenarios considered were below 10-4, however 95th percentile risks were above 10-4 annual probability of infection and 10-6 DALY per person per year for scenarios with improper cooking and lack of gamma irradiation. The greatest difference between microbiological risks for the scenarios tested was observed when comparing proper vs. improper cooking (5 to 6 orders of magnitude) and gamma irradiation (4 to 5 orders of magnitude) compared to (up to less than 1 order of magnitude) for peeling and deveining vs. peeling only. The findings from this research suggest that restriction of Salmonella spp. to low levels (median 5 to 30 per L aquaculture pond water) may be necessary for scenarios in which proper downstream food handling and processing cannot be guaranteed.

Translating research to policy at the NCSE 2017 symposium "Microbiology of the Built Environment: Implications for Health and Design".

Here, we summarize a symposium entitled "Microbiology of the Built Environment: Implications for Health and Design" that was presented at the National Council for Science and the Environment (NCSE) 17th National Conference and Global Forum in January 2017. We covered topics including indoor microbial exposures and childhood asthma, the influence of hospital design on neonatal development, the role of the microbiome in our premise (i.e., building) plumbing systems, antibiotic resistance, and quantitative microbial risk assessment. This symposium engaged the broader scientific and policy communities in a discussion to increase awareness of this critical research area and translate findings to practice.