The Beta Poisson dose-response model is not a single-hit model

Risk Assessment Predicts Most of the Salmonellosis Risk in Raw Chicken Parts is Concentrated in Those Few Products with High Levels of High-Virulence Serotypes of Salmonella

Salmonella prevalence declined in U.S. raw poultry products since adopting prevalence-based Salmonella performance standards, but human illnesses did not reduce proportionally. We used Quantitative Microbial Risk Assessment (QMRA) to evaluate public health risks of raw chicken parts contaminated with different levels of all Salmonella and specific high- and low-virulence serotypes. Lognormal Salmonella level distributions were fitted to 2012 USDA-FSIS Baseline parts survey and 2023 USDA-FSIS HACCP verification sampling data. Three different Dose-Response (DR) approaches included (i) a single DR for all serotypes, (ii) DR that reduces Salmonella Kentucky ST152 virulence, and (iii) multiple serotype-specific DR models. All scenarios found risk concentrated in the few products with high Salmonella levels. Using a single DR model with Baseline data (μ = -3.19, σ = 1.29 Log CFU/g), 68% and 37% of illnesses were attributed to the 0.7% and 0.06% of products with >1 and >10 CFU/g Salmonella, respectively. Using distributions from 2023 HACCP data (μ = -5.53, σ = 2.45), 99.8% and 99.0% of illnesses were attributed to the 1.3% and 0.4% of products with >1 and >10 CFU/g Salmonella, respectively. Scenarios with serotype-specific DR models showed more concentrated risk at higher levels. Baseline data showed 92% and 67% and HACCP data showed >99.99% and 99.96% of illnesses attributed to products with >1 and >10 CFU/g Salmonella, respectively. Regarding serotypes using Baseline or HACCP input data, 0.002% and 0.1% of illnesses were attributed to the 0.2% and 0.4% of products with >1 CFU/g of Kentucky ST152, respectively, while 69% and 83% of illnesses were attributed to the 0.3% and 0.6% of products with >1 CFU/g of Enteritidis, Infantis, or Typhimurium, respectively. Therefore, public health risk in chicken parts is concentrated in finished products with high levels and specifically high levels of high-virulence serotypes. Low-virulence serotypes like Kentucky contribute few human cases.

Public health aspects of Vibrio spp. related to the consumption of seafood in the EU

Vibrio parahaemolyticus, Vibrio vulnificus and non-O1/non-O139 Vibrio cholerae are the Vibrio spp. of highest relevance for public health in the EU through seafood consumption. Infection with V. parahaemolyticus is associated with the haemolysins thermostable direct haemolysin (TDH) and TDH-related haemolysin (TRH) and mainly leads to acute gastroenteritis. V. vulnificus infections can lead to sepsis and death in susceptible individuals. V. cholerae non-O1/non-O139 can cause mild gastroenteritis or lead to severe infections, including sepsis, in susceptible individuals. The pooled prevalence estimate in seafood is 19.6% (95% CI 13.7-27.4), 6.1% (95% CI 3.0-11.8) and 4.1% (95% CI 2.4-6.9) for V. parahaemolyticus, V. vulnificus and non-choleragenic V. cholerae, respectively. Approximately one out of five V. parahaemolyticus-positive samples contain pathogenic strains. A large spectrum of antimicrobial resistances, some of which are intrinsic, has been found in vibrios isolated from seafood or food-borne infections in Europe. Genes conferring resistance to medically important antimicrobials and associated with mobile genetic elements are increasingly detected in vibrios. Temperature and salinity are the most relevant drivers for Vibrio abundance in the aquatic environment. It is anticipated that the occurrence and levels of the relevant Vibrio spp. in seafood will increase in response to coastal warming and extreme weather events, especially in low-salinity/brackish waters. While some measures, like high-pressure processing, irradiation or depuration reduce the levels of Vibrio spp. in seafood, maintaining the cold chain is important to prevent their growth. Available risk assessments addressed V. parahaemolyticus in various types of seafood and V. vulnificus in raw oysters and octopus. A quantitative microbiological risk assessment relevant in an EU context would be V. parahaemolyticus in bivalve molluscs (oysters), evaluating the effect of mitigations, especially in a climate change scenario. Knowledge gaps related to Vibrio spp. in seafood and aquatic environments are identified and future research needs are prioritised.

Parametric analysis of SARS-CoV-2 dose-response models in transportation scenarios

Transportation systems involve high-density crowds of geographically diverse people with variations in susceptibility; therefore, they play a large role in the spread of infectious diseases like SARS-CoV-2. Dose-response models are widely used to model the relationship between the trigger of a disease and the level of exposure in transmission scenarios. In this study, we quantified and bounded viral exposure-related parameters using empirical data from five transportation-related events of SARS-CoV-2 transmission. Dose-response models were then applied to parametrically analyze the infection spread in generic transportation systems, including a single-aisle airplane, bus, and railway coach, and then examined the mitigating efficiency of masks by performing a sensitivity analysis of the related factors. We found that dose level significantly affected the number of secondary infections. In general, we observed that mask usage reduced infection rates at all dose levels and that high-quality masks equivalent to FFP2/N95 masks are effective for all dose levels. In comparison, we found that lower-quality masks exhibit limited mitigation efficiency, especially in the presence of high dosage. The sensitivity analysis indicated that a reduction in the infection distance threshold is a critical factor in mask usage.

Applications of Quantitative Microbial Risk Assessment to Respiratory Pathogens and Implications for Uptake in Policy: A State-of-the-Science Review

BACKGROUND

Respiratory tract infections are major contributors to the global disease burden. Quantitative microbial risk assessment (QMRA) holds potential as a rapidly deployable framework to understand respiratory pathogen transmission and inform policy on infection control.

OBJECTIVES

The goal of this paper was to evaluate, motivate, and inform further development of the use of QMRA as a rapid tool to understand the transmission of respiratory pathogens and improve the evidence base for infection control policies.

METHODS

We conducted a literature review to identify peer-reviewed studies of complete QMRA frameworks on aerosol inhalation or contact transmission of respiratory pathogens. From each of the identified studies, we extracted and summarized information on the applied exposure model approaches, dose-response models, and parameter values, including risk characterization. Finally, we reviewed linkages between model outcomes and policy.

RESULTS

We identified 93 studies conducted in 16 different countries with complete QMRA frameworks for diverse respiratory pathogens, including SARS-CoV-2, Legionella spp., Staphylococcus aureus, influenza, and Bacillus anthracis. Six distinct exposure models were identified across diverse and complex transmission pathways. In 57 studies, exposure model frameworks were informed by their ability to model the efficacy of potential interventions. Among interventions, masking, ventilation, social distancing, and other environmental source controls were commonly assessed. Pathogen concentration, aerosol concentration, and partitioning coefficient were influential exposure parameters as identified by sensitivity analysis. Most (84%, n = 78 ) studies presented policy-relevant content including a) determining disease burden to call for policy intervention, b) determining risk-based threshold values for regulations, c) informing intervention and control strategies, and d) making recommendations and suggestions for QMRA application in policy.

CONCLUSIONS

We identified needs to further the development of QMRA frameworks for respiratory pathogens that prioritize appropriate aerosol exposure modeling approaches, consider trade-offs between model validity and complexity, and incorporate research that strengthens confidence in QMRA results. https://doi.org/10.1289/EHP12695.

Quantitative microbial risk assessment for ingestion of antibiotic resistance genes from private wells contaminated by human and livestock fecal sources

We used quantitative microbial risk assessment to estimate ingestion risk for intI1, erm(B), sul1, tet(A), tet(W), and tet(X) in private wells contaminated by human and/or livestock feces. Genes were quantified with five human-specific and six bovine-specific microbial source-tracking (MST) markers in 138 well-water samples from a rural Wisconsin county. Daily ingestion risk (probability of swallowing ≥1 gene) was based on daily water consumption and a Poisson exposure model. Calculations were stratified by MST source and soil depth over the aquifer where wells were drilled. Relative ingestion risk was estimated using wells with no MST detections and >6.1 m soil depth as a referent category. Daily ingestion risk varied from 0 to 8.8 × 10-1 by gene and fecal source (i.e., human or bovine). The estimated number of residents ingesting target genes from private wells varied from 910 (tet(A)) to 1,500 (intI1 and tet(X)) per day out of 12,000 total. Relative risk of tet(A) ingestion was significantly higher in wells with MST markers detected, including wells with ≤6.1 m soil depth contaminated by bovine markers (2.2 [90% CI: 1.1-4.7]), wells with >6.1 m soil depth contaminated by bovine markers (1.8 [1.002-3.9]), and wells with ≤6.1 m soil depth contaminated by bovine and human markers simultaneously (3.1 [1.7-6.5]). Antibiotic resistance genes (ARGs) were not necessarily present in viable microorganisms, and ingestion is not directly associated with infection. However, results illustrate relative contributions of human and livestock fecal sources to ARG exposure and highlight rural groundwater as a significant point of exposure.IMPORTANCEAntibiotic resistance is a global public health challenge with well-known environmental dimensions, but quantitative analyses of the roles played by various natural environments in transmission of antibiotic resistance are lacking, particularly for drinking water. This study assesses risk of ingestion for several antibiotic resistance genes (ARGs) and the class 1 integron gene (intI1) in drinking water from private wells in a rural area of northeast Wisconsin, United States. Results allow comparison of drinking water as an exposure route for antibiotic resistance relative to other routes like food and recreational water. They also enable a comparison of the importance of human versus livestock fecal sources in the study area. Our study demonstrates the previously unrecognized importance of untreated rural drinking water as an exposure route for antibiotic resistance and identifies bovine fecal material as an important exposure factor in the study setting.

Occurrence, transmission and risks assessment of pathogens in aquatic environments accessible to humans

Pathogens are ubiquitously detected in various natural and engineered water systems, posing potential threats to public health. However, it remains unclear which human-accessible waters are hotspots for pathogens, how pathogens transmit to these waters, and what level of health risk associated with pathogens in these environments. This review collaboratively focuses and summarizes the contamination levels of pathogens on the 5 water systems accessible to humans (natural water, drinking water, recreational water, wastewater, and reclaimed water). Then, we showcase the pathways, influencing factors and simulation models of pathogens transmission and survival. Further, we compare the health risk levels of various pathogens through Quantitative Microbial Risk Assessment (QMRA), and assess the limitations of water-associated QMRA application. Pathogen levels in wastewater are consistently higher than in other water systems, with no significant variation for Cryptosporidium spp. among five water systems. Hydraulic conditions primarily govern the transmission of pathogens into human-accessible waters, while environmental factors such as temperature impact pathogens survival. The median and mean values of computed public health risk levels posed by pathogens consistently surpass safety thresholds, particularly in the context of recreational waters. Despite the highest pathogens levels found in wastewater, the calculated health risk is significantly lower than in other water systems. Except pathogens concentration, variables like the exposure mode, extent, and frequency are also crucial factors influencing the public health risk in water systems. This review shares valuable insights to the more accurate assessment and comprehensive management of public health risk in human-accessible water environments.

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.

Cold tolerance in Campylobacter jejuni and its impact on food safety

Campylobacter jejuni is a major cause of foodborne illnesses worldwide and is primarily transmitted to humans through contaminated poultry meat. To control this pathogen, it is critical to understand its cold tolerance because poultry products are usually distributed in the cold chain. However, there is limited information regarding how this thermotolerant, microaerophilic pathogen can survive in cold and aerobic environments in the poultry cold chain. In this study, we investigated the cold tolerance of C. jejuni by measuring the viability of 90 C. jejuni strains isolated from retail raw chicken at 4 °C under aerobic and microaerobic conditions. Despite the microaerophilic nature of C. jejuni, under aerobic conditions, C. jejuni exhibited higher viability at 4 °C and required an extended inactivation time compared to microaerobic conditions. Some strains were highly tolerant to refrigeration temperatures and exhibited increased survival at 4 °C. These cold-tolerant strains mostly belonged to multilocus sequence typing (MLST) clonal complex (CC)-21 and CC-443, indicating that cold tolerance is associated with the phylogeny of C. jejuni. Notably, cold-tolerant strains had an increased probability of illness and were more likely to cause human infections due to their extended survival on refrigerated chicken meat compared to those sensitive to cold stress. Furthermore, the majority of cold-tolerant strains exhibited elevated aerotolerance, indicating that cold tolerance is related to aerotolerance. These findings suggest that refrigeration of chicken meat under aerobic conditions may not be effective at controlling C. jejuni and that cold-tolerant C. jejuni can pose an increased risk to food safety.

Quantifying the Individual Variation in Susceptibility to Endemic Coronavirus and SARS-CoV-2 with Human Challenge Trials

Human challenge trials reveal how the infection risk depends on a given infectious dose. We propose a mathematical framework to analyze and interpret the outcomes of human challenge trials by incorporating the variability between individuals in susceptibility to infection. We illustrate the framework for two distinctive diseases; endemic diseases where a fraction of the study population has been exposed to the target pathogen previously and is thus immune, and novel diseases where the study population is fully susceptible. Based on available data from published trials, we estimate the immune proportion and the variation in susceptibility to endemic HCoV-229E and present plausible infection risks with SARS-CoV-2 over multiple orders of magnitude of the infectious dose. The results show that the proposed method captures heterogeneous background susceptibility in the study population, and we suggest ways to improve the design of future trials and to translate their outcomes to the general population.

Modelling the effects of antibiotic usage in livestock on human salmonellosis

Antibiotic usage in livestock has been suggested as a driver of antimicrobial resistance in human and livestock populations. This has contributed to the implementation of stewardship programs to curtail usage of antibiotics in livestock. However, the consequences of antibiotic curtailment in livestock on human health are poorly understood. There is the potential for increases in the carriage of pathogens such as Salmonella spp. in livestock, and subsequent increases in human foodborne disease. We use a mathematical model fitted to four case studies, ampicillin and tetracycline usage in fattening pig and broiler poultry populations, to explore the impact of curtailing antibiotic usage in livestock on salmonellosis in humans. Increases in the daily incidence of salmonellosis and a decrease in the proportion of resistant salmonellosis were identified following curtailment of antibiotic usage in livestock. The extent of these increases in human foodborne disease ranged from negligible, to controllable through interventions to target the farm-to-fork pathway. This study provides a motivating example of one plausible scenario following curtailment of antibiotic usage in livestock and suggests that a focus on ensuring good farm-to-fork hygiene and livestock biosecurity is sufficient to mitigate the negative human health consequences of antibiotic stewardship in livestock populations.

A probabilistic-deterministic approach for assessing climate change effects on infection risks downstream of sewage emissions from CSOs

The discharge of pathogens into urban recreational water bodies during combined sewer overflows (CSOs) pose a potential threat for public health which may increase in the future due to climate change. Improved methods are needed for predicting the impact of these effects on the microbiological urban river water quality and infection risks during recreational use. The aim of this study was to develop a novel probabilistic-deterministic modelling approach for this purpose building on physically plausible generated future rainfall time series. The approach consists of disaggregation and validation of daily precipitation time series from 21 regional climate models for a reference period (1971-2000, C20), a near-term future period (2021-2050, NTF) and a long-term future period (2071-2100, LTF) into sub-daily scale, and predicting the concentrations of enterococci and Giardia and Cryptosporidium, and infection risks during recreational use in the river downstream of the sewage emissions from CSOs. The approach was tested for an urban river catchment in Austria which is used for recreational activities (i.e. swimming, playing, wading, hand-to-mouth contact). According to a worst-case scenario (i.e. children bathing in the river), the 95th percentile infection risks for Giardia and Cryptosporidium range from 0.08 % in winter to 8 % per person and exposure event in summer for C20. The infection risk increase in the future is up to 0.8 log10 for individual scenarios. The results imply that measures to prevent CSOs may be needed to ensure sustainable water safety. The approach is promising for predicting the effect of climate change on urban water safety requirements and for supporting the selection of sustainable mitigation measures. Future studies should focus on reducing the uncertainty of the predictions at local scale.

Extension of probability models of the risk of infections by human enteric viruses

This study presents a novel approach for obtaining reliable models and coefficients to estimate the probability of infection caused by common human enteric viruses. The aim is to provide guidance for public health policies in disease prevention and control, by reducing uncertainty and management costs in health risk assessments. Conventional dose-response (DR) models, based on the theory elaborated by Furumoto and Mickey [1], exhibit limitations stemming from the heterogeneity of individual host susceptibilities to infection resulting from ingesting aggregate viruses. Moreover, the scarcity of well-designed viral challenge experiments contributes to significant uncertainty in these DR models. To address these issues, we conducted a review of infection models used in health risk analysis, focusing on Norovirus (NoV) GI.1, pooled Enterovirus group (EV), Poliovirus 1/SM, and Echo-12 virus via contaminated water or food. Using a mechanistic approach, we reevaluated the known DR models and coefficients for the probability of individual host infection in the mentioned viruses based on dose-infection challenge experiments. Specifically, we sought to establish a relationship between the minimum infectious dose (ID) and the ID having a 50% probability of initiating host infection in the same challenge experiment. Furthermore, we developed a new formula to estimate the degree of aggregation of GI.1 NoV at the mean infectious dose. The proposed models, based on "exact" beta-Poisson DR models, effectively predicted infection probabilities from ingestion of both disaggregated and aggregate NoV GI.1. Through a numerical evaluation, we compared the results with the maximum likelihood estimation (MLE) probability obtained from a controlled challenge trial with the NoV GI.1 virus described in the literature, demonstrating the accuracy of our approach. By addressing the indetermination of the unmeasured degree of NoV aggregation in each single infectious dose, our models reduce overestimations and uncertainties in microbial risk assessments. This improvement enhances the management of health risks associated with enteric virus infections.

Assessing the Risk of Seasonal Effects of Campylobacter Contaminated Broiler Meat Prepared In-Home in the United States

Campylobacter has consistently posed a food safety issue in broiler meat. This study aimed to create a quantitative microbial risk assessment model from retail to consumption, designed to evaluate the seasonal risk of campylobacteriosis associated with broiler meat consumption in the United States. To achieve this, data was gathered to build distributions that would enable us to predict the growth of Campylobacter during various stages such as retail storage, transit, and home storage. The model also included potential fluctuations in concentration during food preparation and potential cross-contamination scenarios. A Monte Carlo simulation with 100,000 iterations was used to estimate the risk of infection per serving and the number of infections in the United States by season. In the summer, chicken meat was estimated to have a median risk of infection per serving of 9.22 × 10^-7 and cause an average of about 27,058,680 infections. During the winter months, the median risk of infection per serving was estimated to be 4.06 × 10^-7 and cause an average of about 12,085,638 infections. The risk assessment model provides information about the risk of broiler meat to public health by season. These results will help understand the most important steps to reduce the food safety risks from contaminated chicken products.

Simplified within-host and Dose-response Models of SARS-CoV-2

Understanding the mechanistic dynamics of transmission is key to designing more targeted and effective interventions to limit the spread of infectious diseases. A well-described within-host model allows explicit simulation of how infectiousness changes over time at an individual level. This can then be coupled with dose-response models to investigate the impact of timing on transmission. We collected and compared a range of within-host models used in previous studies and identified a minimally-complex model that provides suitable within-host dynamics while keeping a reduced number of parameters to allow inference and limit unidentifiability issues. Furthermore, non-dimensionalised models were developed to further overcome the uncertainty in estimates of the size of the susceptible cell population, a common problem in many of these approaches. We will discuss these models, and their fit to data from the human challenge study (see Killingley et al. (2022)) for SARS-CoV-2 and the model selection results, which has been performed using ABC-SMC. The parameter posteriors have then used to simulate viral-load based infectiousness profiles via a range of dose-response models, which illustrate the large variability of the periods of infection window observed for COVID-19.

A novel method to jointly estimate transmission rate and decay rate parameters in environmental transmission models

In environmental transmission, pathogens transfer from one individual to another via the environment. It is a common transmission mechanism in a wide range of host-pathogen systems. Incorporating environmental transmission in dynamic transmission models is crucial for gauging the effect of interventions, as extrapolating model results to new situations is only valid when the mechanisms are modelled correctly. The challenge in environmental transmission models lies in not jointly identifiable parameters for pathogen shedding, decay, and transmission dynamics. To solve this unidentifiability issue, we present a stochastic environmental transmission model with a novel scaling method for shedding rate parameter and a novel estimation method that distinguishes transmission rate and decay rate parameters. The core of our scaling and estimation method is calculating exposure and relating exposure to infection risks. By scaling shedding rate parameter, we standardize exposure to pathogens contributed by one infectious individual present during one time interval to one. The standardized exposure leads to a standard definition of transmission rate parameter applicable to scenarios with different decay rate parameters. Hence, we unify direct transmission (large decay rate) and environmental transmission in a continuous manner. More importantly, our exposure-based estimation method can correctly estimate back the transmission rate and the decay rate parameters, while the commonly used trajectory-based method failed. The reason is that exposure-based method gives the correct weight to infection data from previous observation periods. The correct estimation from exposure-based method will lead to more reliable predictions of intervention impact. Using the effect of disinfection as an example, we show how incorrectly estimated parameters may lead to incorrect conclusions about the effectiveness of interventions. This illustrates the importance of correct estimation of transmission rate and decay rate parameters for extrapolating environmental transmission models and predicting intervention effects.

Dose-response relationship of Ralstonia solanacearum and potato in greenhouse and in vitro experiments

Ralstonia solanacearum is the causative agent of bacterial wilt of potato and other vegetable crops. Contaminated irrigation water contributes to the dissemination of this pathogen but the exact concentration or biological threshold to cause an infection is unknown. In two greenhouse experiments, potted potato plants (Solanum tuberosum) were exposed to a single irrigation with 50 mL water (non-invasive soil-soak inoculation) containing no or 102 - 108 CFU/mL R. solanacearum. The disease response of two cultivars, Kondor and HB, were compared. Disease development was monitored over a three-month period after which stems, roots and tubers of asymptomatic plants were analyzed for latent infections. First wilting symptoms were observed 15 days post inoculation in a plant inoculated with 5x109 CFU and a mean disease index was used to monitor disease development over time. An inoculum of 5x105 CFU per pot (1.3x102 CFU/g soil) was the minimum dose required to cause wilting symptoms, while one latent infection was detected at the lowest dose of 5x102 CFU per pot (0.13 CFU/g). In a second set of experiments, stem-inoculated potato plants grown in vitro were used to investigate the dose-response relationship under optimal conditions for pathogen growth and disease development. Plants were inoculated with doses between 0.5 and 5x105 CFU/plant which resulted in visible symptoms at all doses. The results led to a dose-response model describing the relationship between R. solanacearum exposure and probability of infection or illness of potato plants. Cultivar Kondor was more susceptible to brown-rot infections than HB in greenhouse experiments while there was no significant difference between the dose-response models of both cultivars in in vitro experiments. The ED50 for infection of cv Kondor was 1.1x107 CFU. Results can be used in management strategies aimed to reduce or eliminate the risk of bacterial wilt infection when using treated water in irrigation.

Dose response for Salmonella Typhimurium and Enteritidis and other nontyphoid enteric salmonellae

This dose response assessment combines data from 6 human challenge studies and 44 outbreaks to determine infectivity and pathogenicity of several serotypes of nontyphoid Salmonella. Outcomes focus on the major serotypes Salmonella Enteritidis and Typhimurium, showing that Typhimurium is less infectious and has a lower probability of causing acute illness in infected subjects. The dose response relation of Salmonella Enteritidis is less steep than that of Typhimurium, indicating greater heterogeneity in infectivity and pathogenicity. This study revisits an older study with less flexible methods that could not combine the widely different outcomes of challenge studies and outbreaks, and had limited capability for dealing with missing information. Reported outcomes are in a format that allows use in calculations of uncertainty for quantitative risk assessment.

Forty Years of Risk Analysis: A Scientometric Overview

Risk Analysis was first published in 1981, established with a vision to provide a platform for inquiry into fundamental risk-related concepts and theories, and to disseminate new knowledge about methods and approaches for identifying, analyzing, evaluating, managing, and communicating risk. The journal has also contributed significantly to a scientific understanding of specific risks related to human health and safety, engineering, ecological, and social systems. Published on behalf of the Society for Risk Analysis, the journal has become a leading platform over its 40-year history. Complementing recent celebratory overviews and perspectives on the evolution, achievements, and future challenges for Risk Analysis, this article presents a scientometric overview of the journal between 1981 and 2020. The study presents high-level insights in the journal publication trends and structure and trends in the leading countries/regions, institutions, and authors, in relation to their respective collaboration networks. Furthermore, the structure and evolution of research focus issues is analyzed, and highly cited publications are identified. The findings are primarily intended to provide high-level insights, which may be useful for early career academics and risk practitioners to understand the structure and development of the research domain, and its main contributors and topics, and for experienced researchers to reflect on the achievements and future developments.

Capturing complex interactions in disease ecology with simplicial sets

Network approaches have revolutionized the study of ecological interactions. Social, movement and ecological networks have all been integral to studying infectious disease ecology. However, conventional (dyadic) network approaches are limited in their ability to capture higher-order interactions. We present simplicial sets as a tool that addresses this limitation. First, we explain what simplicial sets are. Second, we explain why their use would be beneficial in different subject areas. Third, we detail where these areas are: social, transmission, movement/spatial and ecological networks and when using them would help most in each context. To demonstrate their application, we develop a novel approach to identify how pathogens persist within a host population. Fourth, we provide an overview of how to use simplicial sets, highlighting specific metrics, generative models and software. Finally, we synthesize key research questions simplicial sets will help us answer and draw attention to methodological developments that will facilitate this.

A comprehensive modelling approach to estimate the transmissibility of coronavirus and its variants from infected subjects in indoor environments

A central issue in assessing the airborne risk of COVID-19 infections in indoor spaces pertains to linking the viral load in infected subjects to the lung deposition probability in exposed individuals through comprehensive aerosol dynamics modelling. In this paper, we achieve this by combining aerosol processes (evaporation, dispersion, settling, lung deposition) with a novel double Poisson model to estimate the probability that at least one carrier particle containing at least one virion will be deposited in the lungs and infect a susceptible individual. Multiple emission scenarios are considered. Unlike the hitherto used single Poisson models, the double Poisson model accounts for fluctuations in the number of carrier particles deposited in the lung in addition to the fluctuations in the virion number per carrier particle. The model demonstrates that the risk of infection for 10-min indoor exposure increases from 1 to 50% as the viral load in the droplets ejected from the infected subject increases from 2 × 108 to 2 × 1010 RNA copies/mL. Being based on well-established aerosol science and statistical principles, the present approach puts airborne risk assessment methodology on a sound formalistic footing, thereby reducing avoidable epistemic uncertainties in estimating relative transmissibilities of different coronavirus variants quantified by different viral loads.

Quantitative Risk Assessment of Vibrio parahaemolyticus Toxi Infection Associated with the Consumption of Roasted Shrimp (Penaeus monodon)

In this study, a risk assessment on Vibrio parahaemolyticus infections was carried out in order to estimate the likelihood of gastroenteritis for Cameroonians after consumption of roasted shrimp (Penaeus monodon). The Codex Alimentarius Commission framework was used in this study. Based on the distribution of total V. parahaemolyticus in shrimp and literature information indicating that nonhaemolysing carrier strains could be pathogenic to humans, the cooking, and consumption patterns, the daily exposure level generated in this study, and the dose-response model from other studies, the infectious risk was evaluated and quantified by the Monte Carlo simulation. This simulation was realized based on 10,000 iterations using the Model Risk software, version 4.0, in combination with Microsoft Excel. To better quantify the exposure of consumers and the resulting risk of infection, several scenarios reflecting the minimal, average, and maximal exposures were undertaken. According to the results, the 90% confidence intervals for minimum and maximum exposures ranged from 15 to 24 colony-forming units per day (cells/day) and from 160 to 228 cells/day, respectively. Based on the modal scenario, 90% of the population consuming this shrimp is exposed to V. parahaemolyticus loads ranging from 74 to 110 cells/day, indicating a risk of infection ranging from 1.2 to 1.8 cases per million of consumption. The estimated number of annual disease cases based on annual production is between 1 and 10 cases. This reflects a relatively low risk of infection for roasted shrimp. Good hygiene practices during handling, cooking, and storage may help reduce the actual risk.

Seasonal variation of quantitative microbial risk assessment for three airborne enteric bacteria from wastewater treatment plant emissions

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.

Increased airborne transmission of COVID-19 with new variants, implications for health policies

New COVID-19 variants, either of higher viral load such as delta or higher contagiousness like omicron, can lead to higher airborne transmission than historical strains. This paper highlights their implications for health policies, based on a clear analytical understanding and modeling of the airborne contamination paths, of the dose following exposure, and the importance of the counting unit for pathogens, itself linked to the dose-response law. Using the counting unit of Wells, i.e. the quantum of contagium, we develop the conservation equation of quanta which allows deriving the value of the quantum concentration at steady state for a well-mixed room. The link with the monitoring concentration of carbon dioxide is made and used for a risk analysis of a variety of situations for which we collected CO2 time-series observations. The main conclusions of these observations are that 1) the present norms of ventilation, are both insufficient and not respected, especially in a variety of public premises, leading to high risk of contamination and that 2) air can often be considered well-mixed. Finally, we insist that public health policy in the field of airborne transmission should be based on a multi parameter analysis such as the time of exposure, the quantum production rate, mask wearing and the infector proportion in the population in order to evaluate the risk, considering the whole complexity of dose evaluation. Recognizing airborne transmission requires thinking in terms of time of exposure rather than in terms of proximal distance.

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.

Genetic Microbial Source Tracking Support QMRA Modeling for a Riverine Wetland Drinking Water Resource

Riverine wetlands are important natural habitats and contain valuable drinking water resources. The transport of human- and animal-associated fecal pathogens into the surface water bodies poses potential risks to water safety. The aim of this study was to develop a new integrative modeling approach supported by microbial source tracking (MST) markers for quantifying the transport pathways of two important reference pathogens, Cryptosporidium and Giardia, from external (allochthonous) and internal (autochthonous) fecal sources in riverine wetlands considering safe drinking water production. The probabilistic-deterministic model QMRAcatch (v 1.1 python backwater) was modified and extended to account for short-time variations in flow and microbial transport at hourly time steps. As input to the model, we determined the discharge rates, volumes and inundated areas of the backwater channel based on 2-D hydrodynamic flow simulations. To test if we considered all relevant fecal pollution sources and transport pathways, we validated QMRAcatch using measured concentrations of human, ruminant, pig and bird associated MST markers as well as E. coli in a Danube wetland area from 2010 to 2015. For the model validation, we obtained MST marker decay rates in water from the literature, adjusted them within confidence limits, and simulated the MST marker concentrations in the backwater channel, resulting in mean absolute errors of < 0.7 log₁₀ particles/L (Kruskal–Wallis p > 0.05). In the scenarios, we investigated (i) the impact of river discharges into the backwater channel (allochthonous sources), (ii) the resuspension of pathogens from animal fecal deposits in inundated areas, and (iii) the pathogen release from animal fecal deposits after rainfall (autochthonous sources). Autochthonous and allochthonous human and animal sources resulted in mean loads and concentrations of Cryptosporidium and Giardia (oo)cysts in the backwater channel of 3–13 × 10⁹ particles/hour and 0.4–1.2 particles/L during floods and rainfall events, and in required pathogen treatment reductions to achieve safe drinking water of 5.0–6.2 log₁₀. The integrative modeling approach supports the sustainable and proactive drinking water safety management of alluvial backwater areas.

Modelling the interplay of future changes and wastewater management measures on the microbiological river water quality considering safe drinking water production

Rivers are important for drinking water supply worldwide. However, they are often impacted by pathogen discharges via wastewater treatment plants (WWTP) and combined sewer overflows (CSO). To date, accurate predictions of the effects of future changes and pollution control measures on the microbiological water quality of rivers considering safe drinking water production are hindered due to the uncertainty of the pathogen source and transport variables. The aim of this study was to test an integrative approach for an improved understanding of these effects, i.e. climate change and population growth as well as enhanced treatment at WWTPs and/or prevention of CSOs. We applied a significantly extended version of QMRAcatch (v1.0 Python), a probabilistic-deterministic model that combines fate and transport modelling with quantitative microbial infection risk assessment. The impact of climatic changes until the period 2035-2049 was investigated by a conceptual semi-distributed hydrological model, based on regional climate model outputs. QMRAcatch was calibrated and validated using site- and source-specific data (human-associated genetic microbial source tracking marker and enterovirus). The study showed that the degree to which future changes affect drinking water safety strongly depends on the type and magnitude of faecal pollution sources and are thus highly site- and scenario-specific. For example, if the load of pathogens from WWTPs is reduced through enhanced treatment, climate-change driven increases in CSOs had a considerable impact. Preventing CSOs and installing enhanced treatment at the WWTPs together had the most significant positive effect. The simultaneous consideration of source apportionment and concentrations of reference pathogens, focusing on human-specific viruses (enterovirus, norovirus) and cross-comparison with bacterial and protozoan pathogens (Campylobacter, Cryptosporidium), was found crucial to quantify these effects. While demonstrated here for a large, wastewater-impacted river, the approach is applicable at other catchments and pollution sources. It allows assessing future changes and selecting suitable pollution control measures for long-term water safety planning.

[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.

Navigating Data Uncertainty and Modeling Assumptions in Quantitative Microbial Risk Assessment in an Informal Settlement in Kampala, Uganda

Decision-makers in developing communities often lack credible data to inform decisions related to water, sanitation, and hygiene. Quantitative microbial risk assessment (QMRA), which quantifies pathogen-related health risks across exposure routes, can be informative; however, the utility of QMRA for decision-making is often undermined by data gaps. This work integrates QMRA, uncertainty and sensitivity analyses, and household surveys in Bwaise, Kampala (Uganda) to characterize the implications of censored data management, identify sources of uncertainty, and incorporate risk perceptions to improve the suitability of QMRA for informal settlements or similar settings. In Bwaise, drinking water, hand rinse, and soil samples were collected from 45 households and supplemented with data from 844 surveys. Quantified pathogen (adenovirus, Campylobacter jejuni, and Shigella spp./EIEC) concentrations were used with QMRA to model infection risks from exposure through drinking water, hand-to-mouth contact, and soil ingestion. Health risks were most sensitive to pathogen data, hand-to-mouth contact frequency, and dose-response models (particularly C. jejuni). When managing censored data, results from upper limits of detection, half of limits of detection, and uniform distributions returned similar results, which deviated from lower limits of detection and maximum likelihood estimation imputation approaches. Finally, risk perceptions (e.g., it is unsafe to drink directly from a water source) were identified to inform risk management.

Dose-Response Modeling: Extrapolating From Experimental Data to Real-World Populations

Dose-response modeling of biological agents has traditionally focused on describing laboratory-derived experimental data. Limited consideration has been given to understanding those factors that are controlled in a laboratory, but are likely to occur in real-world scenarios. In this study, a probabilistic framework is developed that extends Brookmeyer's competing-risks dose-response model to allow for variation in factors such as dose-dispersion, dose-deposition, and other within-host parameters. With data sets drawn from dose-response experiments of inhalational anthrax, plague, and tularemia, we illustrate how for certain cases, there is the potential for overestimation of infection numbers arising from models that consider only the experimental data in isolation.

Mathematical Quantification of Transmission in Experiments: FMDV Transmission in Pigs Can Be Blocked by Vaccination and Separation

Quantitative understanding of transmission with and without control measures is important for the control of infectious diseases because it helps to determine which of these measures (or combinations thereof) will be effective to reduce transmission. In this paper, the statistical methods used to estimate transmission parameters are explained. To show how these methods can be used we reviewed literature for papers describing foot-and-mouth disease virus (FMDV) transmission in pigs and we used the data to estimate transmission parameters. The analysis showed that FMDV transmits very well when pigs have direct contact. Transmission, however, is reduced when a physical barrier separates infected and susceptible non-vaccinated pigs. Vaccination of pigs can prevent infection when virus is administered by a single intradermal virus injection in the bulb of the heel, but it cannot prevent infection when pigs are directly exposed to either non-vaccinated or vaccinated FMDV infected pigs. Physical separation combined with vaccination is observed to block transmission. Vaccination and separation can make a significant difference in the estimated number of new infections per day. Experimental transmission studies show that the combined effect of vaccination and physical separation can significantly reduce transmission (R < 1), which is a very relevant result for the control of between-farm transmission.

Demonstrating the Benefits of Predictive Bayesian Dose-Response Relationships Using Six Exposure Studies of Cryptosporidium parvum

A conventional dose-response function can be refitted as additional data become available. A predictive dose-response function in contrast does not require a curve-fitting step, only additional data and presents the unconditional probabilities of illness, reflecting the level of information it contains. In contrast, the predictive Bayesian dose-response function becomes progressively less conservative as more information is included. This investigation evaluated the potential for using predictive Bayesian methods to develop a dose-response for human infection that improves on existing models, to show how predictive Bayesian statistical methods can utilize additional data, and expand the Bayesian methods for a broad audience including those concerned about an oversimplification of dose-response curve use in quantitative microbial risk assessment (QMRA). This study used a dose-response relationship incorporating six separate data sets for Cryptosporidium parvum. A Pareto II distribution with known priors was applied to one of the six data sets to calibrate the model, while the others were used for subsequent updating. While epidemiological principles indicate that local variations, host susceptibility, and organism strain virulence may vary, the six data sets all appear to be well characterized using the Bayesian approach. The adaptable model was applied to an existing data set for Campylobacter jejuni for model validation purposes, which yielded results that demonstrate the ability to analyze a dose-response function with limited data using and update those relationships with new data. An analysis of the goodness of fit compared to the beta-Poisson methods also demonstrated correlation between the predictive Bayesian model and the data.

Microbial Dose-Response Curves and Disinfection Efficacy Models Revisited

The same term “dose-response curve” describes the relationship between the number of ingested microbes or their logarithm, and the probability of acute illness or death (type I), and between a disinfectant’s dose and the targeted microbe’s survival ratio (type II), akin to survival curves in thermal and non-thermal inactivation kinetics. The most common model of type I curves is the cumulative form of the beta-Poisson distribution which is sometimes indistinguishable from the lognormal or Weibull distribution. The most notable survival kinetics models in static disinfection are of the Chick-Watson-Hom’s kind. Their published dynamic versions, however, should be viewed with caution. A microbe population’s type II dose-response curve, static and dynamic, can be viewed as expressing an underlying spectrum of individual vulnerabilities (or resistances) to the particular disinfectant. Therefore, such a curve can be described mathematically by the flexible Weibull distribution, whose scale parameter is a function of the disinfectant’s intensity, temperature, and other factors. But where the survival ratio’s drop is so steep that the static dose-response curve resembles a step function, the Fermi distribution function becomes a suitable substitute. The utility of the CT (or Ct) concept primarily used in water disinfection is challenged on theoretical grounds and its limitations highlighted. It is suggested that stochastic models of microbial inactivation could be used to link the fates of individual viruses or bacteria to their manifestation in the survival curve’s shape. Although the emphasis is on viruses and bacteria, most of the discussion is relevant to fungi, protozoa, and perhaps worms too.

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.

Quantitative Microbial Risk Assessment as support for bathing waters profiling

Profiling bathing waters supported by Quantitative Microbial Risk Assessment (QMRA) is key to the WHO's recommendations for the 2020/2021 revision of the European Bathing Water Directive. We developed an area-specific QMRA model on four pathogens, using fecal indicator concentrations (E. coli, enterococci) for calculating pathogen loads. The predominance of illness was found to be attributable to Human Adenovirus, followed by Salmonella, Vibrio, and Norovirus. Overall, the cumulative illness risk showed a median of around 1 case/10000 exposures. The risk estimates were strongly influenced by the indicators that were used, suggesting the need for a more detailed investigation of the different sources of fecal contamination. Area-specific threshold values for fecal indicators were estimated on a risk-basis by modelling the cumulative risk against E. coli and enterococci concentrations. To improve bathing waters assessment, we suggest considering source apportionment, locally estimating of pathogen/indicator ratios, and calculating site-specific indicators thresholds based on risk assessment.

Noroviruses are highly infectious but there is strong variation in host susceptibility and virus pathogenicity

Noroviruses are a major public health concern: their high infectivity and environmental persistence have been documented in several studies. Genetic sequencing shows that noroviruses are highly variable, and exhibit rapid evolution. A few human challenge studies have been performed with norovirus, leading to estimates of their infectivity. However, such incidental estimates do not provide insight into the biological variation of the virus and the interaction with its human host. To study the variation in infectivity and pathogenicity of norovirus, multiple challenge studies must be analysed jointly, to compare their differences and describe how virus infectivity and host susceptibility vary. Since challenge studies can only provide a small sample of the diversity in the natural norovirus population, outbreaks should be exploited as an additional source of information. The present study shows how challenge studies and 'natural experiments' can be combined in a multilevel dose response framework. Infectivity and pathogenicity are analysed by secretor status as a host factor, and genogroup as a pathogen factor. Infectivity, characterized as the estimated mean infection risk when exposed to 1 genomic copy (qPCR unit)is 0.28 for GI norovirus, and 0.076 for GII virus, both in Se⁺ subjects. The corresponding risks of acute enteric illness are somewhat lower, about 0.2 (GI) and 0.035 (GII), in outbreaks. Se^- subjects are protected, with substantially lower risks of infection (0.00007 and 0.015 at a dose of 1 GC of GI and GII virus, respectively). The present study shows there is considerable variability in risk of infection and especially risk of acute symptoms following infection with norovirus. These challenge and outbreak data consistently indicate high infectivity among secretor positives and protection in secretor negatives.

Pooled samples and eDNA-based detection can facilitate the "clean trade" of aquatic animals

The regional and international trade of live animals facilitates the movement, spillover, and emergence of zoonotic and epizootic pathogens around the world. Detecting pathogens in trade is critical for preventing their continued movement and introduction, but screening a sufficient fraction to ensure rare infections are detected is simply infeasible for many taxa and settings because of the vast numbers of animals involved—hundreds of millions of live animals are imported into the U.S.A. alone every year. Batch processing pools of individual samples or using environmental DNA (eDNA)—the genetic material shed into an organism’s environment—collected from whole consignments of animals may substantially reduce the time and cost associated with pathogen surveillance. Both approaches, however, lack a framework with which to determine sampling requirements and interpret results. Here I present formulae for pooled individual samples (e.g,. swabs) and eDNA samples collected from finite populations and discuss key assumptions and considerations for their use with a focus on detecting Batrachochytrium salamandrivorans, an emerging pathogen that threatens global salamander diversity. While empirical validation is key, these formulae illustrate the potential for eDNA-based detection in particular to reduce sample sizes and help bring clean trade into reach for a greater number of taxa, places, and contexts.

Multilevel model for airborne transmission of foot-and-mouth disease applied to Swedish livestock

The foot-and-mouth disease is an ever-present hazard to the livestock industry due to the huge economic consequences following an outbreak that necessitates culling of possibly infected animals in vast numbers. The disease is highly contagious and previous epizootics have shown that it spreads by many routes. One such route is airborne transmission, which has been investigated in this study by means of a detailed multilevel model that includes all scales of an outbreak. Local spread within an infected farm is described by a stochastic compartment model while the spread between farms is quantified by atmospheric dispersion simulations using a network representation of the set of farms. The model was applied to the Swedish livestock industry and the risk for an epizootic outbreak in Sweden was estimated using the basic reproduction number of each individual livestock-holding farm as the endpoint metric. The study was based on comprehensive official data sets for both the current livestock holdings and regional meteorological conditions. Three species of farm animals are susceptible to the disease and are present in large numbers: cattle, pigs and sheep. These species are all included in this study using their individual responses and consequences to the disease. It was concluded that some parts of southern Sweden are indeed preconditioned to harbor an airborne epizootic, while the sparse farm population of the north renders such events unlikely to occur there. The distribution of the basic reproduction number spans over several orders of magnitudes with low risk of disease spread from the majority of the farms while some farms may act as very strong disease transmitters. The results may serve as basic data in the planning of the national preparedness for this type of events.

Implementation of quantitative microbial risk assessment (QMRA) for public drinking water supplies: Systematic review

In the more than 15 years since its introduction, quantitative microbial risk assessment (QMRA) has become a widely used technique for assessing population health risk posed by waterborne pathogens. However, the variation in approaches taken for QMRA in relation to drinking water supply is not well understood. This systematic review identifies, categorises, and critically synthesises peer-reviewed and academic case studies of QMRA implementation for existing distributed public drinking water supplies. Thirty-nine English-language, peer-reviewed and academic studies published from 2003 to 2019 were identified. Key findings were synthesised in narrative form. The overall designs of the included studies varied widely, as did the assumptions used in risk calculation, especially in relation to pathogen dose. There was also substantial variation in the degree to which the use of location-specific data weighed with the use of assumptions when performing risk calculation. In general, the included studies' complexity did not appear to be associated with greater result certainty. Factors relating to pathogen dose were commonly influential on risk estimates whereas dose-response parameters tended to be of low relative influence. In two of the included studies, use of the 'susceptible fraction' factor was inconsistent with recognised guidance and potentially led to the underestimation of risk. While approaches and assumptions used in QMRA need not be standardised, improvement in the reporting of QMRA results and uncertainties would be beneficial. It is recommended that future authors consider the water supply QMRA reporting checklist developed for the current review. Consideration of the broad types of uncertainty relevant to QMRA is also recommended. Policy-makers should consider emergent discussion on acute microbial health-based targets when setting normative guidelines. The continued representation of QMRA case studies within peer-reviewed and academic literature would also enhance future implementation. Further research is needed on the optimisation of QMRA resourcing given the application context.

Update and review of control options for Campylobacter in broilers at primary production

The 2011 EFSA opinion on Campylobacter was updated using more recent scientific data. The relative risk reduction in EU human campylobacteriosis attributable to broiler meat was estimated for on-farm control options using Population Attributable Fractions (PAF) for interventions that reduce Campylobacter flock prevalence, updating the modelling approach for interventions that reduce caecal concentrations and reviewing scientific literature. According to the PAF analyses calculated for six control options, the mean relative risk reductions that could be achieved by adoption of each of these six control options individually are estimated to be substantial but the width of the confidence intervals of all control options indicates a high degree of uncertainty in the specific risk reduction potentials. The updated model resulted in lower estimates of impact than the model used in the previous opinion. A 3-log10 reduction in broiler caecal concentrations was estimated to reduce the relative EU risk of human campylobacteriosis attributable to broiler meat by 58% compared to an estimate larger than 90% in the previous opinion. Expert Knowledge Elicitation was used to rank control options, for weighting and integrating different evidence streams and assess uncertainties. Medians of the relative risk reductions of selected control options had largely overlapping probability intervals, so the rank order was uncertain: vaccination 27% (90% probability interval (PI) 4-74%); feed and water additives 24% (90% PI 4-60%); discontinued thinning 18% (90% PI 5-65%); employing few and well-trained staff 16% (90% PI 5-45%); avoiding drinkers that allow standing water 15% (90% PI 4-53%); addition of disinfectants to drinking water 14% (90% PI 3-36%); hygienic anterooms 12% (90% PI 3-50%); designated tools per broiler house 7% (90% PI 1-18%). It is not possible to quantify the effects of combined control activities because the evidence-derived estimates are inter-dependent and there is a high level of uncertainty associated with each.

Outbreak-Based Giardia Dose-Response Model Using Bayesian Hierarchical Markov Chain Monte Carlo Analysis

Giardia is a zoonotic gastrointestinal parasite responsible for a substantial global public health burden, and quantitative microbial risk assessment (QMRA) is often used to forecast and manage this burden. QMRA requires dose-response models to extrapolate available dose-response data, but the existing model for Giardia ignores valuable dose-response information, particularly data from several well-documented waterborne outbreaks of giardiasis. The current study updates Giardia dose-response modeling by synthesizing all available data from outbreaks and experimental studies using a Bayesian random effects dose-response model. For outbreaks, mean doses (D) and the degree of spatial and temporal aggregation among cysts were estimated using exposure assessment implemented via two-dimensional Monte Carlo simulation, while potential overreporting of outbreak cases was handled using published overreporting factors and censored binomial regression. Parameter estimation was by Markov chain Monte Carlo simulation and indicated that a typical exponential dose-response parameter for Giardia is r = 1.6 × 10^-2 [3.7 × 10^-3 , 6.2 × 10^-2 ] (posterior median [95% credible interval]), while a typical morbidity ratio is m = 3.8 × 10^-1 [2.3 × 10^-1 , 5.5 × 10^-1 ]. Corresponding (logistic-scale) variance components were σ_r = 5.2 × 10^-1 [1.1 × 10^-1 , 9.6 × 10^-1 ] and σ_m = 9.3 × 10^-1 [7.0 × 10^-2 , 2.8 × 10⁰ ], indicating substantial variation in the Giardia dose-response relationship. Compared to the existing Giardia dose-response model, the current study provides more representative estimation of uncertainty in r and novel quantification of its natural variability. Several options for incorporating variability in r (and m) into QMRA predictions are discussed, including incorporation via Monte Carlo simulation as well as evaluation of the current study's model using the approximate beta-Poisson.

Dose-response and transmission: the nexus between reservoir hosts, environment and recipient hosts

Dose is the nexus between exposure and all upstream processes that determine pathogen pressure, and is thereby an important element underlying disease dynamics. Understanding the relationship between dose and disease is particularly important in the context of spillover, where nonlinearities in the dose-response could determine the likelihood of transmission. There is a need to explore dose-response models for directly transmitted and zoonotic pathogens, and how these interactions integrate within-host factors to consider, for example, heterogeneity in host susceptibility and dose-dependent antagonism. Here, we review the dose-response literature and discuss the unique role dose-response models have to play in understanding and predicting spillover events. We present a re-analysis of dose-response experiments for two important zoonotic pathogens (Middle East respiratory syndrome coronavirus and Nipah virus), to exemplify potential difficulties in differentiating between appropriate models with small exposure experiment datasets. We also discuss the data requirements needed for robust selection between dose-response models. We then suggest how these processes could be modelled to gain more realistic predictions of zoonotic transmission outcomes and highlight the exciting opportunities that could arise with increased collaboration between the virology and epidemiology disciplines. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Synergy between quantitative microbial source tracking (qMST) and quantitative microbial risk assessment (QMRA): A review and prospectus

The use of microbial source tracking (MST) marker genes has grown in recent years due to the need to attribute point and non-point fecal contamination to specific sources. Quantitative microbial risk assessment (QMRA) is a modeling approach used to estimate health risks from exposure to feces-contaminated water and associated pathogens. A combination of these approaches [quantitative MST (qMST) and QMRA] can provide additional pathogen-related information for prioritizing and addressing health risks, compared to reliance on conventional fecal indicator bacteria (FIB). To inform expansion of this approach, a review of published qMST-QMRA studies was conducted to summarize the state of the science and to identify research needs. The reviewed studies primarily aimed to identify what levels of MST marker genes in hypothetical recreational waterbodies would exceed the United States Environmental Protection Agency (USEPA) risk benchmarks for primary contact recreators. The QMRA models calculated relationships between MST marker gene(s) and reference pathogens based on published data in the literature. The development of a robust, accurate relationship was identified as an urgent research gap for qMST-QMRA. This metric requires additional knowledge to quantify the relationship between MST marker genes and the degree of variability in decay of pathogens as a dynamic function of environmental conditions and combinations of fecal sources at multiple spatial and temporal scales. Improved characterization of host shedding rates of host-associated microorganisms (i.e., MST marker genes), as well as fate and transport of these microorganisms and their nucleic acids, would facilitate expansion of this approach to other exposure pathways. Incorporation of information regarding the recovery efficiency, and host-specificity of MST marker genes into QMRA model parameters, and the sensitivity analysis, would greatly improve risk management and site-specific water monitoring criteria.

Quantitative Microbial Risk Assessment for Campylobacter jejuni in Ground Meat Products in Korea

This study evaluated Campylobacter jejuni risk in ground meat products. The C. jejuni prevalence in ground meat products was investigated. To develop the predictive model, survival data of C. jejuni were collected at 4°C-30°C during storage, and the data were fitted using the Weibull model. In addition, the storage temperature and time of ground meat products were investigated during distribution. The consumption amount and frequency of ground meat products were investigated by interviewing 1,500 adults. The prevalence, temperature, time, and consumption data were analyzed by @RISK to generate probabilistic distributions. In 224 samples of ground meat products, there were no C. jejuni-contaminated samples. A scenario with a series of probabilistic distributions, a predictive model and a dose-response model was prepared to calculate the probability of illness, and it showed that the probability of foodborne illness caused by C. jejuni per person per day from ground meat products was 5.68×10-10, which can be considered low risk.

The application of quantitative microbial risk assessment to natural recreational waters: A review

This review examines the aims of and approaches to the Quantitative Microbial Risk Assessment (QMRA) of untreated recreational waters. The literature search was conducted on four databases and yielded 54 papers, which were analyzed on a quantitative (time-trend, geographical distribution, water type) and qualitative (aims, source of microbial data, pathogens and their measurement or estimation, ways to address variability and uncertainty, sensitivity analysis) basis. In addition, the parameters, implications, and limitations were discussed for each QMRA step. Since 2003, the number of papers has greatly increased, highlighting the importance of QMRA for the risk management of recreational waters. Nevertheless, QMRA still exhibits critical issues, above all regarding contamination data and dose-response relationships. To our knowledge, this is the first review to give a wide panoramic view on QMRA in relation to recreational exposure to untreated waters. This could be useful in identifying the current knowledge gaps and research needs.

Confirming the need for virus disinfection in municipal subsurface drinking water supplies

Enteric viruses pose the greatest acute human health risks associated with subsurface drinking water supplies, yet quantitative risk assessment tools have rarely been used to develop health-based targets for virus treatment in drinking water sourced from these supplies. Such efforts have previously been hampered by a lack of consensus concerning a suitable viral reference pathogen and dose-response model as well as difficulties in quantifying pathogenic viruses in water. A reverse quantitative microbial risk assessment (QMRA) framework and quantitative polymerase chain reaction data for norovirus genogroup I in subsurface drinking water supplies were used herein to evaluate treatment needs for such water supplies. Norovirus was not detected in over 90% of samples, which emphasizes the need to consider the spatially and/or temporally intermittent patterns of enteric pathogen contamination in subsurface water supplies. Collectively, this analysis reinforces existing recommendations that a minimum 4-log treatment goal is needed for enteric viruses in groundwater in absence of well-specific monitoring information. This result is sensitive to the virus dose-response model used as there is approximately a 3-log discrepancy among virus dose-response models in the existing literature. This emphasizes the need to address the uncertainties and lack of consensus related to various QMRA modelling approaches and the analytical limitations that preclude more accurate description of virus risks.

Human health risk assessment for the occurrence of enteric viruses in drinking water from wells: Role of flood runoff injections

We demonstrated that floods can induce severe microbiological contamination of drinking water from wells and suggest strategies to better address water safety plans for groundwater drinking supplies. Since 2002, the Italian Water Research Institute (IRSA) has detected hepatitis A virus, adenovirus, rotavirus, norovirus, and enterovirus in water samples from wells in the Salento peninsula, southern Italy. Perturbations in the ionic strength in water flow can initiate strong virus detachments from terra rossa sediments in karst fractures. This study therefore explored the potential health impacts of prolonged runoff injections in Salento groundwater caused by severe flooding during October 2018. A mathematical model for virus fate and transport in fractures was applied to determine the impact of floodwater injection on groundwater quality by incorporating mechanisms that affect virus attachment/detachment and survival in flowing water at microscale. This model predicted target concentrations of enteric viruses that can occur unexpectedly in wells at considerable distances (5-8 km) from the runoff injection site (sinkhole). Subsequently, the health impact of viruses in drinking water supplied from contaminated wells was estimated during the summer on the Salento coast. Specific unpublished dose-response model coefficients were proposed to determine the infection probabilities for Echo-11 and Polio 1 enteroviruses through ingestion. The median (50%) risk of infection was estimated at 6.3 · 10^-3 with an uncertainty of 23%. The predicted burden of diseases was 4.89 disability adjusted life years per year, i.e., twice the maximum tolerable disease burden. The results highlight the requirement for additional water disinfection treatments in Salento prior to the distribution of drinking water. Moreover, monthly controls of enteric virus occurrence in water from wells should be imposed by a new water framework directive in semiarid regions because of the vulnerability of karst carbonate aquifers to prolonged floodwater injections and enteric virus contamination.

Quantitative Assessment of the Health Risk for Livestock When Animal Viruses Are Applied in Human Oncolytic Therapy: A Case Study for Seneca Valley Virus

Some viruses cause tumor regression and can be used to treat cancer patients; these viruses are called oncolytic viruses. To assess whether oncolytic viruses from animal origin excreted by patients pose a health risk for livestock, a quantitative risk assessment (QRA) was performed to estimate the risk for the Dutch pig industry after environmental release of Seneca Valley virus (SVV). The QRA assumed SVV excretion in stool by one cancer patient on Day 1 in the Netherlands, discharge of SVV with treated wastewater into the river Meuse, downstream intake of river water for drinking water production, and consumption of this drinking water by pigs. Dose-response curves for SVV infection and clinical disease in pigs were constructed from experimental data. In the worst scenario (four log₁₀ virus reduction by drinking water treatment and a farm with 10,000 pigs), the infection risk is less than 1% with 95% certainty. The risk of clinical disease is almost seven orders of magnitude lower. Risks may increase proportionally with the numbers of treated patients and days of virus excretion. These data indicate that application of wild-type oncolytic animal viruses may infect susceptible livestock. A QRA regarding the use of oncolytic animal virus is, therefore, highly recommended. For this, data on excretion by patients, and dose-response parameters for infection and clinical disease in livestock, should be studied.

A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model

Current efforts to assess human health response to chemicals based on high-throughput in vitro assay data on intra-cellular changes have been hindered for some illnesses by lack of information on higher-level extracellular, inter-organ, and organism-level interactions. However, a dose-response function (DRF), informed by various levels of information including apical health response, can represent a template for convergent top-down, bottom-up analysis. In this paper, a general DRF for chronic chemical and other health stressors and mixtures is derived based on a general first-order model previously derived and demonstrated for illness progression. The derivation accounts for essential autocorrelation among initiating event magnitudes along a toxicological mode of action, typical of complex processes in general, and reveals the inverse relationship between the minimum illness-inducing dose, and the illness severity per unit dose (both variable across a population). The resulting emergent DRF is theoretically scale-inclusive and amenable to low-dose extrapolation. The two-parameter single-toxicant version can be monotonic or sigmoidal, and is demonstrated preferable to traditional models (multistage, lognormal, generalized linear) for the published cancer and non-cancer datasets analyzed: chloroform (induced liver necrosis in female mice); bromate (induced dysplastic focia in male inbred rats); and 2-acetylaminofluorene (induced liver neoplasms and bladder carcinomas in 20,328 female mice). Common- and dissimilar-mode mixture models are demonstrated versus orthogonal data on toluene/benzene mixtures (mortality in Japanese medaka, Oryzias latipes, following embryonic exposure). Findings support previous empirical demonstration, and also reveal how a chemical with a typical monotonically-increasing DRF can display a J-shaped DRF when a second, antagonistic common-mode chemical is present. Overall, the general DRF derived here based on an autocorrelated first-order model appears to provide both a strong theoretical/biological basis for, as well as an accurate statistical description of, a diverse, albeit small, sample of observed dose-response data. The further generalizability of this conclusion can be tested in future analyses comparing with traditional modeling approaches across a broader range of datasets.