QMRA for Drinking Water: 2. The Effect of Pathogen Clustering in Single-Hit Dose-Response Models

Signal and noise in metabarcoding data

Metabarcoding is a powerful molecular tool for simultaneously surveying hundreds to thousands of species from a single sample, underpinning microbiome and environmental DNA (eDNA) methods. Deriving quantitative estimates of underlying biological communities from metabarcoding is critical for enhancing the utility of such approaches for health and conservation. Recent work has demonstrated that correcting for amplification biases in genetic metabarcoding data can yield quantitative estimates of template DNA concentrations. However, a major source of uncertainty in metabarcoding data stems from non-detections across technical PCR replicates where one replicate fails to detect a species observed in other replicates. Such non-detections are a special case of variability among technical replicates in metabarcoding data. While many sampling and amplification processes underlie observed variation in metabarcoding data, understanding the causes of non-detections is an important step in distinguishing signal from noise in metabarcoding studies. Here, we use both simulated and empirical data to 1) suggest how non-detections may arise in metabarcoding data, 2) outline steps to recognize uninformative data in practice, and 3) identify the conditions under which amplicon sequence data can reliably detect underlying biological signals. We show with both simulations and empirical data that, for a given species, the rate of non-detections among technical replicates is a function of both the template DNA concentration and species-specific amplification efficiency. Consequently, we conclude metabarcoding datasets are strongly affected by (1) deterministic amplification biases during PCR and (2) stochastic sampling of amplicons during sequencing-both of which we can model-but also by (3) stochastic sampling of rare molecules prior to PCR, which remains a frontier for quantitative metabarcoding. Our results highlight the importance of estimating species-specific amplification efficiencies and critically evaluating patterns of non-detection in metabarcoding datasets to better distinguish environmental signal from the noise inherent in molecular detections of rare targets.

Current assumptions for quantitative microbial risk assessment (QMRA) of Norovirus contamination of drinking water catchments due to recreational activities: an update

Contamination of drinking water from Norovirus (NoV) and other waterborne viruses is a major public health concern globally. Increasingly, quantitative microbial risk assessment (QMRA) is being used to assess the various risks from waterborne pathogens and evaluate control strategies. As urban populations grow and expand, there is increasing demand for recreational activities in drinking water catchments. QMRA relies on context-specific data to map out the pathways by which viruses can enter water and be transferred to drinking water consumers and identify risk factors and appropriate controls. This review examines the current evidence base and assumptions for QMRA analysis of NoV and other waterborne viral pathogens and recommends numerical values based on the most recent evidence to better understand the health risks associated with recreators in Australian drinking water sources; these are broadly applicable to all drinking water sources where recreational access is allowed. Key issues include the lack of an agreed upon data and dose-response models for human infectious NoV genotypes, faecal shedding by bathers, the extent of NoV infectivity and aggregation, resistance (secretor status) to NoV and the extent of secondary transmission.

Statewide Quantitative Microbial Risk Assessment for Waterborne Viruses, Bacteria, and Protozoa in Public Water Supply Wells in Minnesota

Infection risk from waterborne pathogens can be estimated via quantitative microbial risk assessment (QMRA) and forms an important consideration in the management of public groundwater systems. However, few groundwater QMRAs use site-specific hazard identification and exposure assessment, so prevailing risks in these systems remain poorly defined. We estimated the infection risk for 9 waterborne pathogens based on a 2-year pathogen occurrence study in which 964 water samples were collected from 145 public wells throughout Minnesota, USA. Annual risk across all nine pathogens combined was 3.3 × 10^-1 (95% CI: 2.3 × 10^-1 to 4.2 × 10^-1), 3.9 × 10^-2 (2.3 × 10^-2 to 5.4 × 10^-2), and 1.2 × 10^-1 (2.6 × 10^-2 to 2.7 × 10^-1) infections person^-1 year^-1 for noncommunity, nondisinfecting community, and disinfecting community wells, respectively. Risk estimates exceeded the U.S. benchmark of 10^-4 infections person^-1 year^-1 in 59% of well-years, indicating that the risk was widespread. While the annual risk for all pathogens combined was relatively high, the average daily doses for individual pathogens were low, indicating that significant risk results from sporadic pathogen exposure. Cryptosporidium dominated annual risk, so improved identification of wells susceptible to Cryptosporidium contamination may be important for risk mitigation.

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.

Describing water treatment process performance: Why average log-reduction can be a misleading statistic

The degree to which a technology used for drinking water treatment physically removes or inactivates pathogenic microorganisms is commonly expressed as a log-reduction (or log-removal) and is of central importance to the provision of microbiologically safe drinking water. Many evaluations of water treatment process performance generate or compile multiple values of microorganism log-reduction, and it is common to report the average of these log-reduction values as a summary statistic. This work provides a cautionary note against misinterpretation and misuse of averaged log-reduction values by mathematically proving that the average of a set of log-reduction values characteristically overstates the average performance of which the set of log-reduction values is believed to be representative. This has two important consequences for drinking water and food safety as well as other applications of log-reduction: 1) a technology with higher average log-reduction does not necessarily have higher average performance, and 2) risk analyses using averaged log-reduction values as point estimates of treatment efficiency will underestimate average risk-sometimes by well over an order of magnitude. When analyzing a set of log-reduction values, a summary statistic called the effective log-reduction (which averages reduction or passage rates and expresses this as a log-reduction) provides a better representation of average performance of a treatment technology.

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.

Some aspects of deep-bed filtration dynamics in QMRA for drinking water

Unlike most unit processes in drinking water treatment, the performance of deep-bed filtration processes vary systematically on short time-scales; the particle removal capacity changes with time since the previous backwash, even when the influent water quality is stable. For microorganisms, the removal efficiency may vary by orders of magnitude. In this note, the potential impact of such dynamics on microbial risk estimates is studied, using representative experimental filtration data for viruses and bacteria in conjunction with single-hit dose-response models for microbial infection. Assuming that filtration is the only source of variation in pathogen concentrations on the time-scale of a single filter cycle, it is concluded that such variations are unlikely to substantially affect risk estimates, except possibly in an outbreak situation with extremely high pathogen concentrations; it is generally sufficient to know the mean pathogen concentrations. Future studies should include concurrent variation in the performance of other unit processes and raw water pathogen concentrations. Experimental work should focus on capturing the variation in filtration performance in order to correctly estimate mean removal rates.

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.

Coagulant residues' influence on virus enumeration as shown in a study on virus removal using aluminium, zirconium and chitosan

Research on microorganism reduction by physicochemical water treatment is often carried out under the assumption that the microbiological enumeration techniques are not affected by the presence of coagulants. Data presented here indicate that bacteriophage enumeration by plaque assay and RT-qPCR (reverse transcription quantitative polymerase chain reaction) can be affected by these water treatment chemicals. Treatment of water samples with an alkaline protein-rich solution prior to plaque assay and optimization of RNA extraction for RT-qPCR were implemented to minimize the interference. The improved procedures were used in order to investigate reduction of three viral pathogens and the MS2 model virus in the presence of three coagulants. A conventional aluminium coagulant was compared to alternative agents (zirconium and chitosan) in a coagulation-filtration system. The highest virus reduction, i.e., 99.9-99.99%, was provided by chitosan, while aluminium and zirconium reduced virus by 99.9% in colour-rich water and by 90% in water with less colour, implying an effect of coagulant type and raw water quality on virus reduction. Although charge characteristics of viruses were associated with virus reduction, the results reveal that the MS2 phage is a suitable model for aggregation and retention of the selected pathogens.

Dose-response relationships for environmentally mediated infectious disease transmission models

Environmentally mediated infectious disease transmission models provide a mechanistic approach to examining environmental interventions for outbreaks, such as water treatment or surface decontamination. The shift from the classical SIR framework to one incorporating the environment requires codifying the relationship between exposure to environmental pathogens and infection, i.e. the dose-response relationship. Much of the work characterizing the functional forms of dose-response relationships has used statistical fit to experimental data. However, there has been little research examining the consequences of the choice of functional form in the context of transmission dynamics. To this end, we identify four properties of dose-response functions that should be considered when selecting a functional form: low-dose linearity, scalability, concavity, and whether it is a single-hit model. We find that i) middle- and high-dose data do not constrain the low-dose response, and different dose-response forms that are equally plausible given the data can lead to significant differences in simulated outbreak dynamics; ii) the choice of how to aggregate continuous exposure into discrete doses can impact the modeled force of infection; iii) low-dose linear, concave functions allow the basic reproduction number to control global dynamics; and iv) identifiability analysis offers a way to manage multiple sources of uncertainty and leverage environmental monitoring to make inference about infectivity. By applying an environmentally mediated infectious disease model to the 1993 Milwaukee Cryptosporidium outbreak, we demonstrate that environmental monitoring allows for inference regarding the infectivity of the pathogen and thus improves our ability to identify outbreak characteristics such as pathogen strain.

QMRA for Drinking Water: 1. Revisiting the Mathematical Structure of Single-Hit Dose-Response Models

Dose-response models are essential to quantitative microbial risk assessment (QMRA), providing a link between levels of human exposure to pathogens and the probability of negative health outcomes. In drinking water studies, the class of semi-mechanistic models known as single-hit models, such as the exponential and the exact beta-Poisson, has seen widespread use. In this work, an attempt is made to carefully develop the general mathematical single-hit framework while explicitly accounting for variation in (1) host susceptibility and (2) pathogen infectivity. This allows a precise interpretation of the so-called single-hit probability and precise identification of a set of statistical independence assumptions that are sufficient to arrive at single-hit models. Further analysis of the model framework is facilitated by formulating the single-hit models compactly using probability generating and moment generating functions. Among the more practically relevant conclusions drawn are: (1) for any dose distribution, variation in host susceptibility always reduces the single-hit risk compared to a constant host susceptibility (assuming equal mean susceptibilities), (2) the model-consistent representation of complete host immunity is formally demonstrated to be a simple scaling of the response, (3) the model-consistent expression for the total risk from repeated exposures deviates (gives lower risk) from the conventional expression used in applications, and (4) a model-consistent expression for the mean per-exposure dose that produces the correct total risk from repeated exposures is developed.