Beta-Poisson Model Research Articles

A beta-Poisson model for infectious disease transmission.

Outbreaks of emerging and zoonotic infections represent a substantial threat to human health and well-being. These outbreaks tend to be characterised by highly stochastic transmission dynamics with intense variation in transmission potential between cases. The negative binomial distribution is commonly used as a model for transmission in the early stages of an epidemic as it has a natural interpretation as the convolution of a Poisson contact process and a gamma-distributed infectivity. In this study we expand upon the negative binomial model by introducing a beta-Poisson mixture model in which infectious individuals make contacts at the points of a Poisson process and then transmit infection along these contacts with a beta-distributed probability. We show that the negative binomial distribution is a limit case of this model, as is the zero-inflated Poisson distribution obtained by combining a Poisson-distributed contact process with an additional failure probability. We assess the beta-Poisson model's applicability by fitting it to secondary case distributions (the distribution of the number of subsequent cases generated by a single case) estimated from outbreaks covering a range of pathogens and geographical settings. We find that while the beta-Poisson mixture can achieve a closer to fit to data than the negative binomial distribution, it is consistently outperformed by the negative binomial in terms of Akaike Information Criterion, making it a suboptimal choice on parsimonious grounds. The beta-Poisson performs similarly to the negative binomial model in its ability to capture features of the secondary case distribution such as overdispersion, prevalence of superspreaders, and the probability of a case generating zero subsequent cases. Despite this possible shortcoming, the beta-Poisson distribution may still be of interest in the context of intervention modelling since its structure allows for the simulation of measures which change contact structures while leaving individual-level infectivity unchanged, and vice-versa.

Quantitative microbiological spoilage risk assessment (QMSRA) of fresh poultry fillets during storage at retail

A quantitative microbiological spoilage risk assessment model (QMSRA) of aerobically stored fresh poultry fillets was developed based on pseudomonads growth and metabolic activity. Simultaneous microbiological and sensory analyses were performed in poultry fillets to evaluate the relation between pseudomonads concentration and sensory rejection due to spoilage. The analysis showed no organoleptic rejection at pseudomonads concentrations less than 6.08 log CFU/cm2. For higher concentrations, a “spoilage-response” relationship was developed using a beta-Poisson model. The above relationship was combined with a stochastic modeling approach for pseudomonads growth by taking into account both variability and uncertainty of factors affecting spoilage. To enhance the reliability of the developed QMSRA model, uncertainty was quantified and separated from variability using a second order Monte Carlo simulation. For a batch of 10,000 units, the QMSRA model predicted a median number of 11, 80, 295, 733 and 1,389 spoiled units for retail storage times of 6,7, 8, 9 and 10 days, respectively, while no spoiled units were predicted for storage time of up to 5 days at retail. Scenario analysis showed that a reduction of 1 log in the pseudomonads concentration at the time of packaging or 1 °C in retail storage temperature results in up to 90% reduction of the spoiled units while the combination of the above interventions can reduce the risk of spoilage by up to 99%, depending on the storage time. The poultry industry can utilize the QMSRA model as a transparent scientific basis to support food quality management decisions in determining appropriate expiration dates which maximize the utilization of the product’s “true” shelf life while minimize the risk of spoilage to an acceptable level. Furthermore, the scenario analysis can provide the necessary components for an effective cost-benefit analysis, enabling the identification and comparison of appropriate strategies for extending the shelf life of fresh poultry products.

Quantitative Risk Assessment of Hepatitis a Virus Infection Arising from the Consumption of Fermented Clams in South Korea.

This study estimated the risk of hepatitis A virus (HAV) foodborne illness outbreaks through the consumption of fermented clams in South Korea. HAV prevalence in fermented clams was obtained from the Ministry of Food and Drug Safety Report, 2019. Fermented clam samples (2 g) were inoculated with HAV and stored at -20-25 °C. Based on the HAV titer (determined using plaque assay) in fermented clams according to storage, the Baranyi predictive models provided by Combase were applied to describe the kinetic behavior of HAV in fermented clams. The initial estimated HAV contamination level was -3.7 Log PFU/g. The developed predictive models revealed that, when the temperature increased, the number of HAV plaques decreased. The Beta-Poisson model was chosen for determining the dose-response of HAV, and the simulation revealed that there was a 6.56 × 10-11/person/day chance of contracting HAV foodborne illness by eating fermented clams. However, when only regular consumers of fermented clams were assumed as the population, the probability of HAV foodborne illness increased to 8.11 × 10-8/person/day. These results suggest that, while there is a low likelihood of HAV foodborne illness from consuming fermented clams across the country, regular consumers should be aware of the possibility of foodborne illness.

BACTERIOLOGICAL QUALITY AND MICROBIAL HEALTH RISKS ASSOCIATED WITH FRESH-FROZEN AND DRIED-PROCESSED READY-TO-SELL PRAWNS (MACROBRACHIUM ROSENBERGII)

This study set out to determine the bacteriological quality and the microbial health risks associated with ready-to-sell prawns. Fresh-frozen prawns were obtained from supermarkets and dried-processed prawns were obtained from local markets in Akure, Nigeria (n = 120). Levels of E. coli, faecal coliforms, Shigella, Salmonella and Staphylococcus aureus in the samples were determined using standard microbiological methods. Probabilities of infection (Pi) associated with exposure to Salmonella and Shigella were determined using beta-Poisson model and for S. aureus exponential model was adopted. Mean level of Salmonella was 0.9 log10 CFU 100 g-1 in fresh-frozen prawns whereas mean levels of Shigella in fresh-frozen and dried-processed prawns were 4.4 and 4.5 log10 CFU 100 g-1 respectively. Probability of infection with exposure to a single CFU of Salmonella, Shigella and S. aureus was predicted at 1.6 × 10-7, 1.9 × 10-4 and 7.6 × 10-8 respectively. This study demonstrates that the levels of bacterial contamination of the prawns and the risks per serving per year were unacceptable.

Assessing the impact of COVID-19 restrictions on the microbial quality of an urban water catchment and the associated probability of waterborne infections

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.

Development and application of a dose-response model for Elizabethkingia spp.

Elizabethkingia spp. are common environmental pathogens responsible for infections in more vulnerable populations. Although the exposure routes of concern are not well understood, some hospital-associated outbreaks have indicated possible waterborne transmission. In order to facilitate quantitative microbial risk assessment (QMRA) for Elizabethkingia spp., this study fit dose-response models to frog and mice datasets that evaluated intramuscular and intraperitoneal exposure to Elizabethkingia spp. The frog datasets could be pooled, and the exact beta-Poisson model was the best fitting model with optimized parameters α =0.52 and β =86,351. Using the exact beta-Poisson model, the dose of Elizabethkingia miricola resulting in a 50% morbidity response (LD50) was estimated to be approximately 237,000 CFU. The model developed herein was used to estimate the probability of infection for a hospital patient under a modeled exposure scenario involving a contaminated medical device and reported Elizabethkingia spp. concentrations isolated from hospital sinks after an outbreak. The median exposure dose was approximately 3 CFU/insertion event, and the corresponding median risk of infection was 3.4E-05. The median risk estimated in this case study was lower than the 3% attack rate observed in a previous outbreak, however, there are noted gaps pertaining to the possible concentrations of Elizabethkingia spp. in tap water and the most likely exposure routes. This is the first dose-response model developed for Elizabethkingia spp. thus enabling future risk assessments to help determine levels of risk and potential effective risk management strategies.

Quantitative risk assessment of staphylococcal enterotoxin A (SEA) in pork in metropolitan Bangkok, Thailand

Staphylococcus aureus (SA) food poisoning is caused by the intake of staphylococcal enterotoxin (SE) produced during SA growth in contaminated food. To better evaluate the risk of SA intoxication, the relationship between SE dose and SA intoxication as a dose response (DR) model in the hazard characterization of risk assessment is needed. The objectives of this study were to propose a best-fit DR model based on SE doses in the outbreaks (secondary data) using the principle of maximum likelihood and to assess the risk of SE in pork through probabilistic models. From the goodness-of-fit test, the deviances of exponential, beta-Poisson, and logistic regression model were 5.66, 5.66, and 3.41, respectively. The best-fit DR model was the logistic regression model with the parameters q1 and q2 of 2.90 and 0.97, respectively. The uncertainty of parameters of DR model was represented by 95% confidence contour and the comparison DR curves of the best-fit and 95% likelihood-based confidence limits for the logistic regression model. The mean annual risk estimates of SE intoxication in pork of the Bangkokian population was in the range of 5.7 × 10−2 and 9.0 × 10−1.

Enteric Pathogens Risk Factors Associated with Household Drinking Water: A Case Study in Ugu District Kwa-Zulu Natal Province, South Africa.

The occurrence of diarrheal infections depends on the level of water and sanitation services available to households of immunocompromised individuals and children of less than five years old. It is therefore of paramount importance for immunocompromised individuals to be supplied with safe drinking water for better health outcomes. The current study aimed at ascertaining the probability of infection that Escherichia coli, Salmonella typhimurium, Shigella dysenteriae, Vibrio cholerae, and rotavirus might cause to rural dwellers as compared to urban dwellers. Both culture-based and molecular-based methods were used to confirm the presence of target microorganisms in drinking water samples, while Beta-Poisson and exponential models were used to determine the health risk assessment. Results revealed the presence of all targeted organisms in drinking water. The estimated health risks for single ingestion of water for the test pathogens were as follows: 1.6 × 10−7 for S. typhimurium, 1.79 × 10−4 for S. dysenteriae, 1.03 × 10−3 for V. cholerae, 2.2 × 10−4 for E. coli O157:H7, and 3.73 × 10−2 for rotavirus. The general quantitative risk assessment undertaken in this study suggests that constant monitoring of household container-stored water supplies is vital as it would assist in early detection of microbial pathogens. Moreover, it will also allow the prompt action to be taken for the protection of public health, particularly for immunocompromised individuals and children who are prone to higher risk of infections.

Development of a dose-response model for porcine cysticercosis.

Taenia solium is an important cause of acquired epilepsy worldwide and remains endemic in Asia, Africa, and Latin America. Transmission of this parasite is still poorly understood despite the design of infection experiments to improve our knowledge of the disease, with estimates for critical epidemiological parameters, such as the probability of human-to-pig infection after exposure to eggs, still lacking. In this paper, a systematic review was carried out and eight pig infection experiments were analyzed to describe the probability of developing cysts. These experiments included different pathways of inoculation: with ingestion of proglottids, eggs, and beetles that ingested eggs, and direct injection of activated oncospheres into the carotid artery. In these experiments, different infective doses were used, and the numbers of viable and degenerated cysts in the body and brain of each pig were registered. Five alternative dose-response models (exponential, logistic, log-logistic, and exact and approximate beta-Poisson) were assessed for their accuracy in describing the observed probabilities of cyst development as a function of the inoculation dose. Dose-response models were developed separately for the presence of three types of cysts (any, viable only, and cysts in the brain) and considered for each of the four inoculation methods ("Proglottids", "Eggs", "Beetles" and "Carotid"). The exact beta-Poisson model best fit the data for the three types of cysts and all relevant exposure pathways. However, observations for some exposure pathways were too scarce to reliably define a dose-response curve with any model. A wide enough range of doses and sufficient sample sizes was only found for the "Eggs" pathway and a merged "Oral" pathway combining the "Proglottids", "Eggs" and "Beetles" pathways. Estimated parameter values from this model suggest that a low infective dose is sufficient to result in a 50% probability for the development of any cyst or for viable cyst infections. Although this is a preliminary model reliant on a limited dataset, the parameters described in this manuscript should contribute to the design of future experimental infections related to T. solium transmission, as well as the parameterization of simulation models of transmission aimed at informing control.

Preliminary quantitative risk assessment of the effect of Salmonella on human health in retail fresh pork

A quantitative microbiological risk assessment was conducted for Salmonella spp. in fresh pork from retail stores to consumers in China in order to provide evidence for adopting effective risk management measures. The national food safety and risk monitoring data was used to estimate the initial contamination level of Salmonella in the retail fresh pork. The growth model and the cross-contamination model of Salmonella were referenced from the ComBase data base and scientific literature, respectively. Then the human health risk was predicted by the consumption of Salmonella-contaminated fresh pork and the Beta-Poisson dose-response model. The number of Salmonellosis per year among 1 million residents due to fresh pork was estimated to be 4748.The sensitivity analysis showed that the risk of Salmonellosis was mainly and positively correlated with the preparation rate of ready to eat food, the consumption quantity of pork, the initial contamination level of Salmonella in fresh pork, and the proportion of improper cleaning of knives and chopping boards. When the contamination level of Salmonella in fresh pork is controlled below 1 CFU/g, the average probability of illness will be reduced by 59.39%; the average probability of illness will be reduced by 58.97% by increasing the ratio of complete cleaning of chopping boards or the separation ratio of chopping boards for raw and ready-to-eat food to 90%. The risk could be effectively reduced by controlling the concentration of Salmonella contamination in retail pork or improving hygiene practices.

Dose–response modelling of infectious animal diseases coupled with computational fluid dynamics: A simulation of airborne porcine reproductive and respiratory syndrome virus

Dose–response modelling was performed for several infectious disease viruses using the exponential, exact beta-Poisson, and approximate beta-Poisson models using existing datasets of animal infections. The exact beta-Poisson model was found to provide the best fit of the three models. The aerosol transmission route was found to require the lowest dose to cause infection compared to the intra-nasal and oral transmission routes for porcine reproductive and respiratory syndrome virus (PRRSV) and H9N2 avian influenza virus. Foot and mouth disease virus required lower doses with the aerosol transmission route than the intra-nasal transmission route. Liquid oral transmission required lower doses than feed transmission to cause infection for African swine fever virus and overall oral transmission required low doses to cause infection by porcine epidemic and diarrhoea virus. A computational fluid dynamics simulation was coupled with the dose–response modelling to simulate aerosol transmission of PRRSV in two experimental case studies. The coupled model predicted a probability of infection between 82% and 100% using the exact beta-Poisson model, which was in good agreement with the observed rate of infections in the two experimental case studies (84% and 90%).

Risk Factor Analysis of Children's Exposure to Microbial Pathogens in Playgrounds.

Bacteria are commonly found in soil and may cause health risks to children playing in the outdoor playgrounds with soil, mainly via hand to mouth and pica behaviors. Our study concerned with the risk analysis of infection of a child playing in urban playgrounds in the cities of Patras and Pyrgos in Greece. The presence of Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa were analyzed in soil samples of these playgrounds. A standardized questionnaire depicted the individual characteristics of each playground and recorded risk factors in playgrounds related to bacterial infections. Furthermore, the distributions of E. coli, S. aureus and P. aeruginosa were analyzed in soil samples. Our results were investigated with beta-Poisson models using the Quantitative Microbial Risk Assessment wiki models to evaluate and construct a probability model of infection for each of these bacteria. The risk of infection was higher during the wet period. The risk was higher for P. aeruginosa infection compared to E. coli and S. aureus ones. Nevertheless, the bacterial concentration was higher for E. coli than P. aeruginosa and S. aureus in both wet and dry periods. Our results provide new data that could contribute in assessing the risks associated with playgrounds where children can unaware play in urban parks.

Application of the log-Logistic Model to Dose Response Relation in Microbial Risk Assessment

Microbial risk assessment in food safety is a valuable tool to reduce the risks of infection by pathogens. The dose-response relation is aimed to establish the relationship between the dose of a pathogen that populations are exposed to and the probability of the adverse health effect by the pathogen. Among many dose-response models ever proposed, the exponential and beta-Poisson models have been internationally applied, but the decision on which model is selected between them solely depends on the goodness of fit to specific data sets. On the other hands, the log-logistic model, one of the alternative models, has been little studied on the dose-response relation. In the present study, thus, the application of the log-logistic model to dose-response relation was studied with hypothetical and experimental data sets of infection (or death), comparing to the above two models. Here the experimental data sets were for pathogenic organisms such as pathogenic Escherichia coli, Listeria monocytogenes, and Cryptosporidium pavrum. Consequently, this model successfully fit to those data sets in comparison to the two models. These results suggested that log-logistic model would have the potential to apply to the dose-response relation, similar to the exponential and beta-Poisson models.

SimBPDD: Simulating differential distributions in Beta-Poisson models, in particular for single-cell RNA sequencing data

Beta-Poisson (BP) models employ Poisson distributions, where the corresponding rate parameter itself is a Beta-distributed random variable. They have been shown to appropriately mimic gene expression distributions in the context of single-cell ribonucleic acid sequencing (scRNA-seq), a breakthrough technology allowing to sequence information from individual biological cells and facilitating fundamental insights into numerous fields of biology. A prominent scRNA-seq data analysis task is to identify differences in gene expression distributions across two conditions. To validate new statistical approaches in this context, one typically has to rely on accurate simulations, as usually no ground truth for an assessment is available. We introduce several simulation procedures that allow to generate differential distributions (DDs) based on BP models. In particular, we describe how to create different types of DDs, mirroring various sources or origins of a difference, and different degrees of DDs, from a weak to a strong difference. The soundness of the simulation procedures is shown in a validation study in which theoretically expected model properties of the DD simulations are confirmed. The findings are in principle not restricted to the scRNA-seq context and may be generally applicable also to other application areas. The simulation approaches are implemented in the publicly available R package SimBPDD.

Modeling the Dose Response Relationship of Waterborne Acanthamoeba.

This study developed dose response models for determining the probability of eye or central nervous system infections from previously conducted studies using different strains of Acanthamoeba spp. The data were a result of animal experiments using mice and rats exposed corneally and intranasally to the pathogens. The corneal inoculations of Acanthamoeba isolate Ac 118 included varied amounts of Corynebacterium xerosis and were best fit by the exponential model. Virulence increased with higher levels of C. xerosis. The Acanthamoeba culbertsoni intranasal study with death as an endpoint of response was best fit by the beta-Poisson model. The HN-3 strain of A. castellanii was studied with an intranasal exposure and three different endpoints of response. For all three studies, the exponential model was the best fit. A model based on pooling data sets of the intranasal exposure and death endpoint resulted in an LD50 of 19,357 amebae. The dose response models developed in this study are an important step towards characterizing the risk associated with free-living amoeba like Acanthamoeba in drinking water distribution systems. Understanding the human health risk posed by free-living amoeba will allow for quantitative microbial risk assessments that support building design decisions to minimize opportunities for pathogen growth and survival.

Ebola Virus Dose Response Model for Aerosolized Exposures: Insights from Primate Data.

This study develops dose-response models for Ebolavirus using previously published data sets from the open literature. Two such articles were identified in which three different species of nonhuman primates were challenged by aerosolized Ebolavirus in order to study pathology and clinical disease progression. Dose groups were combined and pooled across each study in order to facilitate modeling. The endpoint of each experiment was death. The exponential and exact beta-Poisson models were fit to the data using maximum likelihood estimation. The exact beta-Poisson was deemed the recommended model because it more closely approximated the probability of response at low doses though both models provided a good fit. Although transmission is generally considered to be dominated by person-to-person contact, aerosolization is a possible route of exposure. If possible, this route of exposure could be particularly concerning for persons in occupational roles managing contaminated liquid wastes from patients being treated for Ebola infection and the wastewater community responsible for disinfection. Therefore, this study produces a necessary mathematical relationship between exposure dose and risk of death for the inhalation route of exposure that can support quantitative microbial risk assessment aimed at informing risk mitigation strategies including personal protection policies against occupational exposures.

Analyzing Single- and Two-parameter Models for Describing Oil Recovery in Imbibition from Fractured Reservoirs

The imbibition process is known as one of the main production mechanisms in fractured reservoirs where oil/gas-filled matrix blocks are surrounded by water-filled fractures. Different forces such as gravity and capillary play a role in production from a fractured reservoir during imbibition and complicate the imbibition process. In previous works, single-parameter models such as the Aronofsky model and Lambert W function were presented to model imbibition recovery from matrix blocks. The Aronofsky model underestimates early time recovery and overestimates late time recovery, and Lambert W function is suitable for water wet cases. In this work, a data bank of different experimental and numerical imbibition recovery curves at various rock and fluid properties were collected. Then, a rigorous analysis was performed on the models utilized to describe oil/gas recovery during the imbibition process. In addition to investigating the single-parameter models, two-parameter models used for dose-response modeling, including Weibull, beta-Poisson, and Logit models were examined. The results of this work demonstrate that using two-parameter models can improve the prediction of imbibition behavior. Moreover, among the two-parameter models, the Weibull has the capability to describe the imbibition process better. The Aronofsky model underestimates early time recovery and overestimates late time recovery, and Lambert W function is suitable for water wet cases. In this work, a data bank of different experimental and numerical imbibition recovery curves at various rock and fluid properties were collected. Then, a rigorous analysis was performed on the models utilized to describe oil/gas recovery during the imbibition process. In addition to investigating the single-parameter models, two-parameter models used for dose-response modeling, including Weibull, beta-Poisson, and Logit models were examined. The results of this work demonstrate that using two-parameter models can improve the prediction of imbibition behavior. Moreover, among the two-parameter models, the Weibull has the capability to describe the imbibition process better.

Assessment of replicate numbers for titrating avian influenza virus using dose-response models.

Embryonating chicken eggs (ECEs) are among the most sensitive laboratory host systems for avian influenza virus (AIV) titration, but ECEs are expensive and require space for storage and incubation. Therefore, reducing ECE use would conserve resources. We utilized statistical modeling to evaluate the accuracy and precision of AIV titration with 3 instead of 5 ECEs for each dilution by the Reed-Muench method for 50% endpoint calculation. Beta-Poisson and exponential dose-response models were used in a simulation study to evaluate observations from actual titration data from 18 AIV isolates. The reproducibility among replicates of a titration was evaluated with one AIV isolate titrated in 3 replicates with the beta-Poisson, exponential, and Weibull dose-response models. The standard deviation (SD) of the error between input and estimated virus titers was estimated with Monte Carlo simulations using the fitted dose-response models. Good fit was observed with all models that were utilized. Reducing the number of ECEs per dilution from 5 to 3 resulted in the width of the 95% confidence interval increasing from ±0.64 to ±0.75 log10 50% ECE infectious doses (EID50) and the SD of the error increased by 0.03 log10 EID50. Our study suggests that using fewer ECEs per dilution is a viable approach that will allow laboratories to reduce costs and improve efficiency.

Preliminary quantitative microbial risk assessment of pathogenic Vibrio parahaemolyticus in short mackerel in Malaysia

A preliminary quantitative microbial risk assessment of Vibrio parahaemolyticus in short mackerels was conducted from retail to consumption levels in a stochastic model. The key uncertainties included of growth during retail display, transportation duration, and microbial log reduction after the preparation and cooking steps were taken into consideration in the exposure assessment. Human health risk associated with the consumption of short mackerels contaminated with pathogenic V. parahaemolyticus was predicted by fitting the number of cells ingested into a Beta-Poisson dose-response model. Advanced sensitivity analysis was performed to determine the predictive factors most highly correlated with risk illness. The model evaluated through Monte Carlo simulation revealed an estimated number of illness ranged from 1.34 × 10−4 to 12.80 with an average incidence of 2.07 cases per annum. The rates per 100,000 population were predicted at 6.53 × 10−3 cases per year in Malaysia. In this study, the development of quantitative microbial risk assessment of V. parahaemolyticus in short mackerels provides valuable information such as the probability of illness associated with the consumption of short mackerels and the primary factor quantitatively important for risk illness.

Safety risks associated with dispersal of E. coli O157:H7 in home sprouting modules

Despite numerous outbreaks of foodborne illnesses associated with sprouts, consumption of raw sprouted seeds is still linked with a healthy lifestyle in Scandinavia. Different types of sprouting modules for home sprout production are available on the market, and sold without information about risks associated with sprouting. During sprouting, conditions are ideal for seed emergence, but also for bacterial proliferation. Moreover, since seeds used for sprouting are non-sterile, human pathogens may propagate within sprouting modules. This study assessed vertical dispersal of Escherichia coli strain O157:H7 in two types of three-layered sprouting modules intended for home sprouting. Organic fenugreek seeds were artificially inoculated and transferred to either the top or bottom layer of sprouting modules. As commensal bacterial biota can affect survival of an artificially introduced strain, but can also contain pathogens, culturable bacterial species on fenugreek seeds were evaluated. A Beta-Poisson model was used to assess the risk of consumer exposure to the artificially introduced strain when added to seeds in the top or bottom layer. We found that the inoculated strain dispersed vertically up and down between the layers and that the risk of bacterial dispersal was high in either direction, posing a high risk of consumer exposure.