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The use of masks as a measure to control the spread of respiratory viruses has been widely acknowledged. However, there are instances where wearing a mask is not possible, making these environments potential vectors for virus transmission. Such environments can contain multiple sources of infection and are challenging to characterize in terms of infection risk. To address this issue, we have developed a methodology to investigate the role of ventilation in reducing the infection risk in such environments. We use a restaurant setting as a representative scenario to demonstrate the methodology. Using implicit large eddy simulations along with discrete droplet dispersion modeling we investigate the impact of ventilation and physical distance on the spread of respiratory viruses and the risk of infection. Our findings show that operating ventilation systems, such as mechanical mixing and increasing physical distance between subjects, can significantly reduce the average room infection risk and number of newly infected subjects. However, this observation is subject to the transmissibility of the airborne viruses. In the case of a highly transmissible virus, the use of mechanical mixing may be inconsequential when compared to only fresh air ventilation. These findings provide valuable insights into the mitigation of infection risk in situations where the use of masks is not possible.

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Introduction: Whole genome sequencing (WGS) is increasingly used for characterizing foodborne pathogens and it has become a standard typing technique for surveillance and research purposes. WGS data can help assessing microbial risks and defining risk mitigating strategies for foodborne pathogens, including Salmonella enterica. Methods: To test the hypothesis that (combinations of) different genes can predict the probability of infection [P(inf)] given exposure to a certain pathogen strain, we determined P(inf) based on invasion potential of 87 S. enterica strains belonging to 15 serovars isolated from animals, foodstuffs and human patients, in an in vitro gastrointestinal tract (GIT) model system. These genomes were sequenced with WGS and screened for genes potentially involved in virulence. A random forest (RF) model was applied to assess whether P(inf) of a strain could be predicted based on the presence/absence of those genes. Moreover, the association between P(inf) and biofilm formation in different experimental conditions was assessed. Results and Discussion: P(inf) values ranged from 6.7E-05 to 5.2E-01, showing variability both among and within serovars. P(inf) values also varied between isolation sources, but no unambiguous pattern was observed in the tested serovars. Interestingly, serovars causing the highest number of human infections did not show better ability to invade cells in the GIT model system, with strains belonging to other serovars displaying even higher infectivity. The RF model did not identify any virulence factor as significant P(inf) predictors. Significant associations of P(inf) with biofilm formation were found in all the different conditions for a limited number of serovars, indicating that the two phenotypes are governed by different mechanisms and that the ability to form biofilm does not correlate with the ability to invade epithelial cells. Other omics techniques therefore seem more promising as alternatives to identify genes associated with P(inf), and different hypotheses, such as gene expression rather than presence/absence, could be tested to explain phenotypic virulence [P(inf)].

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BACKGROUND: The Wells-Riley equation has been extensively used to quantify the infection risk of airborne transmission indoors. This equation is difficult to apply to actual conditions because it requires measurement of the outdoor air supply rate, which vary with time and are difficult to quantify. The method of determining the fraction of inhaled air that has been exhaled previously by someone in a building using a CO2 concentration measurement can solve the limitations of the existing method. Using this method, the indoor CO2 concentration threshold can be determined to keep the risk of infection below certain conditions. METHODS: Based on the calculation of the rebreathed fraction, an appropriate mean indoor CO2 concentration and required air exchange rate to control SARS-CoV-2 airborne transmission was calculated. The number of indoor occupants, ventilation rate, and the deposition and inactivation rates of the virus-laden aerosols were considered. The application of the proposed indoor CO2 concentration-based infection rate control was investigated through case studies in school classrooms and restaurants. RESULTS: In a typical school classroom environment with 20-25 occupants and an exposure time of 6-8 h, the average indoor CO2 concentration should be kept below 700 ppm to control the risk of airborne infection indoors. The ASHRAE recommended ventilation rate is sufficient when wearing a mask in classrooms. For a typical restaurant with 50-100 occupants and an exposure time of 2-3 h, the average indoor CO2 concentration should be kept below about 900 ppm. Residence time in the restaurant had a significant effect on the acceptable CO2 concentration. CONCLUSION: Given the conditions of the occupancy environment, it is possible to determine an indoor CO2 concentration threshold, and keeping the CO2 concentration lower than a certain threshold could help reduce the risk of COVID-19 infection.

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The World Health Organization reported that COVID-19 cases reached 611,421,786 globally by September 23, 2022. Six months after the first reported case, the disease had spread rapidly, reaching pandemic status, leading to numerous preventive measures to curb the spread, including a complete shutdown of many activities worldwide. Such restrictions affected services like waste management, resulting in waste accumulation in many communities and increased water pollution. Therefore, the current study investigated if lockdown impacted surface water microbial quality within an urban water catchment in South Africa. Using quantitative microbial risk assessment, the study further assessed changes in the probability of infection (Pi) with gastrointestinal illnesses from exposure to polluted water in the catchment. Escherichia coli data for 2019, 2020 and 2021 - pre-COVID, lockdown, and post-lockdown periods, respectively - were collected from the area's wastewater treatment management authorities. The Pi was determined using a beta-Poisson model. Mean overall E. coli counts ranged from 2.93 ± 0.16 to 5.30 ± 1.07 Log10 MPN/100 mL. There was an overall statistically significant increase in microbial counts from 2019 to 2021. However, this difference was only accounted for between 2019 and 2021 (p = 0.008); the increase was insignificant between 2019 and 2020, and 2020 and 2021. The Pi revealed a similar trend for incidental ingestion of 100 mL and 1 mL of polluted water. No statistically significant difference was observed between the years based on multiple exposures. Although the overall microbial load and Pi estimated within the catchment exceeded the local and international limits recommended for safe use by humans, especially for drinking and recreation, these were not significantly affected by the COVID-19 restrictions. Nevertheless, these could still represent a health hazard to immunocompromised individuals using such water for personal and household hygiene, especially in informal settlements without access to water and sanitation services.

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As virus-laden aerosols can accumulate and remain suspended for hours in insufficiently ventilated enclosed spaces, indoor environments can heavily contribute to the spreading of airborne infections. In the COVID-19 pandemics, the role possibly played by cable cars has attracted media attention following several outbreaks in ski resort. To assess the real risk of infection, we experimentally characterize the natural ventilation in cable cars and develop a general stochastic model of infection in an arbitrary indoor space that accounts for the epidemiological situation, the virological parameters, and the indoor characteristics (ventilation rate and occupant number density). As a results of the high air exchange rate (we measured up to 180 air changes per hour) and the relatively short duration of the journey, the infection probability in cable cars traveling with open windows is remarkably lower than in other enclosed spaces such as aircraft cabins, train cars, offices, classrooms, and dining rooms. Accounting for the typical duration of the stay, the probability of infection during a cable-car ride is lower by two to three orders of magnitude than in the other examples considered (the highest risk being estimated in case of a private gathering in a poorly ventilated room). For most practical purposes, the infection probability can be approximated by the inhaled viral dose, which provides an upper bound and allows a simple comparison between different indoor situations once the air exchange rate and the occupant number density are known. Our approach and findings are applicable to any indoor space in which the viral transmission is predominately airborne and the air is well mixed.

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This study introduces a new approach for the investigation of infections after an accidental ingestion of contaminated floodwater. The concept of Expected Annual Probability of Infection (EAPI) is introduced and implemented in an infection risk-model approach, by combining a Quantitative Microbial Risk Assessment (QMRA) with the four steps in flood risk assessment. Two groups and exposure paths are considered: adults wading in floodwater and small children swimming/playing in floodwater. The study area is located in Ghana, West Africa. Even though Ghana is one of the most urbanized countries in Africa it has significant problems with water resources management and public health. While cholera is classified as endemic in Accra, the natural and human-made characteristics of the capital makes it prone to flooding. The results of the EAPI approach show that on one hand the concentration of pathogens in floodwater, and thus the risk of infection, decreases with the increase of the flood magnitude. On the other hand, larger floods can spread the pathogens further from the point source, threatening populations previously not identified as at risk by small-scale floods. The concept of EAPI is demonstrated for cholera but it can be extended to other waterborne diseases and also different pathways of exposure, requiring minimal adaptations. For future applications, better estimation of EAPI key components and improvement points are discussed and recommendations given for all the assessment steps.

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Airborne E. coli, fecal coliform, and Enterococcus are all related to sewage worker's syndrome and therefore used as target enteric bioaerosols about researches in wastewater treatment plants (WWTPs). However, most of the studies are often inadequately carried out because they lack systematic studies reports bioaerosols emission characteristics and health risk assessments for these three enteric bacteria during seasonal variation. Therefore, quantitative microbial risk assessment based on Monte Carlo simulation was utilized in this research to assess the seasonal variations of health risks of the three enteric bioaerosols among exposure populations (academic visitors, field engineers, and office staffs) in a WWTP equipped with rotating-disc and microporous aeration modes. The results show that the concentrations of the three airborne bacteria from the rotating-disc aeration mode were 2-7 times higher than the microporous aeration mode. Field engineers had health risks 1.5 times higher than academic visitors due to higher exposure frequency. Health risks of airborne Enterococcus in summer were up to 3 times higher than those in spring and winter. Similarly, health risks associated to E. coli aerosol exposure were 0.3 times higher in summer compared to spring. In contrast, health risks associated with fecal coliform aerosol were between 2 and 19 times lower in summer compared to spring and winter seasons. Data further suggest that wearing of N95 mask could minimize health risks by 1-2 orders of magnitude. This research shed light on seasonal variation of health risks associated with bioaerosol emission from wastewater utilities.

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Control programmes against non-regulated infectious diseases of farm animals are widely implemented. Different control programmes have different definitions of "freedom from infection" which can lead to difficulties when trading animals between countries. When a disease is still present, in order to identify herds that are safe to trade with, estimating herd-level probabilities of being infected when classified "free from infection" using field data is of major interest. Our objective was to evaluate the capacity of a Bayesian Hidden Markov Model, which computes a herd-level probability of being infected, to detect infected herds compared to using test results only. Herd-level risk factors, infection dynamics and associated test results were simulated in a population of herds, for a wide range of realistic infection contexts and test characteristics. The model was used to predict the infection status of each herd from longitudinal data: a simulated risk factor and a simulated test result. Two different indexes were used to categorize herds from the probability of being infected into a herd predicted status. The model predictive performances were evaluated using the simulated herd status as the gold standard. The model detected more infected herds than a single final test in 85 % of the scenarios which converged. The proportion of infected herds additionally detected by the model, compared to test results alone, varied depending on the context. It was higher in a context of a low herd test sensitivity. On average, around 20 %, for high test sensitivity scenarios, and 40 %, for low test sensitivity scenarios, of infected herds that were undetected by the test were accurately classified as infected by the model. Model convergence did not occur for 39 % of the scenarios, mainly in association with low herd test sensitivity. Detection of additional newly infected herds was always associated with an increased number of false positive herds (except for one scenario). The number of false positive herds was lower for scenarios with low herd test sensitivity and moderate to high incidence and prevalence. These results highlight the benefit of the model, in particular for control programmes with infection present at an endemic level in a population and reliance on test(s) of low sensitivity.

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Bayesian finite mixture models, frequently referred to as Bayesian latent class models have become increasingly common for diagnostic test data in the absence of a gold standard test. Most Bayesian analyses in the veterinary literature have dealt with a dichotomised diagnostic outcome. The use of Bayesian finite mixture models for continuous test outcomes, such as sample to positive (S/P) ratios produced by an ELISA, is much less common, despite continuous models taking advantage of all of the information captured in the test outcome. This paper revisits the idea of the Bayesian finite mixture model and provides a practical guide for researchers who would like to use this approach for modelling continuous diagnostic outcomes as it preserves all information from the observed data. Synthetic datasets and a dataset from literature were analysed to illustrate that a mixture model with continuous diagnostic outcomes can be used to estimate true prevalence and to evaluate test sensitivity and specificity. In addition, directly modelling the continuous test outcomes rather than dichotomising them, means that optimal cut-offs can be defined based on the test purpose rather than being determined before testing. Moreover, as animals with higher scores are more likely to be infected, using continuous data allows test interpretation to be made at the individual animal level. In contrast, dichotomization treats all animals above a cut-off as having the same infection risk. This study demonstrates that dichotomisation is not a 'must' when using Bayesian latent class analysis for diagnostic test data, and suggests that latent class analysis using continuous test outcomes should be favoured when evaluating veterinary diagnostic tests producing continuous outcomes.

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BACKGROUND: The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks. OBJECTIVE: This paper describes a novel model for COVID-19 infection risks and policy evaluations. METHODS: The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific inter-agent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies. RESULTS: The application of the model is demonstrated for a typical office environment and for a real-world case. CONCLUSION: The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.

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The objective of the study was to develop a predictive model of Salmonella spp. growth in pasteurized liquid egg white (LEW) and to estimate the salmonellosis risk using the baseline model and scenario analysis. Samples were inoculated with six strains of Salmonella, and bacterial growth was observed during storage at 10-37 °C. The primary models were developed using the Baranyi model for LEW. For the secondary models, the obtained specific growth rate (µmax) and lag phase duration were fitted to a square root model and Davey model, respectively, as functions of temperature (R2 ≥ 0.98). For µmax, the values were satisfied within an acceptable range (Af, Bf: 0.70-1.15). The probability of infection (Pinf) due to the consumption of LEW was zero in the baseline model. However, scenario analysis suggested possible salmonellosis for the consumption of LEW. Because Salmonella spp. proliferated much faster in LEW than in egg white (EW) during storage at 20 and 30 °C (p < 0.01), greater Pinf may be obtained for LEW when these products are stored at the same conditions. The developed predictive model can be applied to the risk management of Salmonella spp. along the food chain, including during product storage and distribution.

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The lack of proper gastrointestinal models assessing the inter-strain virulence variability of foodborne pathogens and the effect of the vehicle (food matrix) affects the risk estimation. This research aimed to propose a dynamic and integrated in vitro/ex vivo gastrointestinal model to evaluate the probability and severity of infection of foodborne pathogens at different matrices. An everted gut sac was used to determine the adhesion and invasion of Salmonella enterica and tissue damage. S. Typhimurium ATCC 14028 was used as a representative bacterium, and two matrices (water and cheese) were used as vehicles. No differences (p > 0.05) in the probability of infection (Pinf) were found for intra-experimental repeatability. However, the Pinf of cheese-vehiculated S. Typhimurium was different compared to water- vehiculated S. Typhimurium, 7.2-fold higher. The histological analysis revealed Salmonella-induced tissue damage, compared with the control (p < 0.05). In silico proposed interactions between two major Salmonella outer membrane proteins (OmpA and Rck) and digested peptides from cheese casein showed high binding affinity and stability, suggesting a potential protective function from the food matrix. The results showed that the everted gut sac model is suitable to evaluate the inter-strain virulence variability, considering both physiological conditions and the effect of the food matrix.

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Nonnegligible emission of bioaerosols usually occurs during aeration of wastewater in aerator tanks in wastewater treatment plants (WWTPs). Literature had shown that the respiratory and intestinal diseases of workers at WWTPs are related to bioaerosols. Thus, quantitative microbial risk assessment (QMRA) based on Monte Carlo simulation was utilized in this research to assess the health risks of Gram-negative bacteria bioaerosol (GNBB) and Staphylococcus aureus bioaerosol (SAB) among academic visitors and staffs. Results showed that the concentrations of GNBB and SAB in the inverted umbrella aeration mode were consistently higher than those in the microporous aeration mode under all six size distribution ranges of the Anderson six-stage impactor. Thus, GNBB and SAB can be highly threatening to the weasand and first bronchus (or alveoli and third bronchus) for the exposure populations. The health risks (annual probability of infection (Py) and disease burden (DB)) of males were constantly higher than those of females for each certain exposure scenario. The health risks of staffs were higher than those of academic visitors when assessed by Monte Carlo simulation. The wearing of mask is an effective measure to minimize health risks through reducing the bioaerosol concentration intake. Especially, for the academic visitors and staffs exposed to GNBB, all their DB failed to meet the World Health Organization DB benchmark under various credible intervals when they were without a mask on. In a word, the results of health risk assessment in this research can be utilized as an educational tool and policy basis to facilitate the implementation of efficacious prevention measures to protect the public health from bioaerosol health threats in WWTPs.

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BACKGROUND: As of 24th of August 2020, the number of global COVID-19 confirmed cases is nearly 24 million. In the same period, the number of recorded infections in Thailand has remained at approximately 3300. This paper explores the specifics of COVID-19 or SARS-CoV-2 transmissions in Phuket, Thailand's second most visited tourist destination. METHODS: High-risk contacts recorded by Phuket Provincial Public Health Office were analysed using the Probit model to investigate the risk factors for transmission from confirmed COVID-19 cases to their high-risk contacts. The analysis was further focused on the impact of quarantine measures in state provided facilities on contacts' probability of infection. FINDINGS: 15.6% of 1108 high-risk contacts were found to be infected, and they accounted for 80% of 214 confirmed cases in Phuket till 29th April 2020. Moreover, 10.68% of all high-risk contacts were confirmed to be infected before the quarantine, and 4.55% after the policy was enforced. In addition, a contact who lived within the same household with a confirmed case was 25% more exposed to infection when compared to a contact who did not share a household. INTERPRETATION: Results confirmed that the quarantine policy, which mandated individual isolation in the state provided facilities for all high-risk contacts, diminished contact's chance of infection from the confirmed cases, especially in the epicenter districts. Our findings confirmed that sharing accommodation with an infected case, and exposure to a case with several documented secondary transmission, generally increased the SARS-CoV-2 infection probability. Finally, some confirmed cases do exhibit a higher risk of spreading SARS-CoV-2 to their contacts compared to a typical confirmed case. Further studies of high reproduction groups of infected patients are recommended. FUNDING: No funding was received for this research.