Learning Something From Nothing: The Critical Importance of Rethinking Microbial Non-detects

Simplified and Detailed Evaluations of the Uncertainty of the Measurement of Microbiological Contamination of Pharmaceutical Products

BACKGROUND

The control of the microbial contamination of pharmaceutical products (PP) is crucial to ensure their safety and efficacy. The validity of the monitoring of such contamination depends on the uncertainty of this quantification. Highly uncertain quantifications due to the variability of determinations or the magnitude of systematic effects affecting microbial growth or other analytical operations make analysis unfit for the intended use. The quantification of the measurement uncertainty expressing the combined effects of all random and systematic effects affecting the analysis allows for a sound decision about quantification adequacy for their intended use. The complexity of the quantification of microbial analysis uncertainty led to the development of simplified ways of performing this evaluation.

OBJECTIVE

This work assesses the adequacy of the simplified quantification of the uncertainty of the determination of the microbial contamination of PP by log transforming microbial count and dilution factor of the test sample whose uncertainty is combined in a log scale using the uncertainty propagation law.

METHODS

This assessment is performed by a parallel novel bottom-up and accurate evaluation of microbial analysis uncertainty involving the Monte Carlo method simulation of the Poisson log-normal distribution of counts and of the normally distributed measured volumes involved in the analysis. Systematic effects are assessed and corrected on results to compensate for their impact on the determinations. Poisson regression is used to predict precision affecting determinations on unknown test samples.

RESULT

Simplified and detailed models of the uncertainty of the measurement of the microbial contamination of PP are provided, allowing objective comparisons of several determinations and those with a maximum contamination level.

CONCLUSIONS

This work concludes that triplicate determinations are required to produce results with adequately low uncertainty and that simplified uncertainty quantification underevaluates or overevaluates the uncertainty from determinations based on low or high colony numbers, respectively. Therefore, detailed uncertainty evaluations are advised for determinations between 50 and 200% of PP's maximum admissible contamination value.

HIGHLIGHT

User-friendly tools for detailed and simplified evaluations of the uncertainty of the measurement of microbial contamination of PP are provided together with the understanding of when simplifications are adequate.

Drinking water QMRA and decision-making: Sensitivity of risk to common independence assumptions about model inputs

When assessing risk posed by waterborne pathogens in drinking water, it is common to use Monte Carlo simulations in Quantitative Microbial Risk Assessment (QMRA). This method accounts for the variables that affect risk and their different values in a given system. A common underlying assumption in such analyses is that all random variables are independent (i.e., one is not associated in any way with another). Although the independence assumption simplifies the analysis, it is not always correct. For example, treatment efficiency can depend on microbial concentrations if changes in microbial concentrations either affect treatment themselves or are associated with water quality changes that affect treatment (e.g., during/after climate shocks like extreme precipitation events or wildfires). Notably, the effects of erroneous assumptions of independence in QMRA have not been widely discussed. Due to the implications of drinking water safety decisions on public health protection, it is critical that risk models accurately reflect the context being studied to meaningfully support decision-making. This work illustrates how dependence between pathogen concentration and either treatment efficiency or water consumption can impact risk estimates using hypothetical scenarios of relevance to drinking water QMRA. It is shown that the mean and variance of risk estimates can change substantially with different degrees of correlation. Data from a water supply system in Calgary, Canada are also used to illustrate the effect of dependence on risk. Recognizing the difficulty of obtaining data to empirically assess dependence, a framework to guide evaluation of the effect of dependence is presented to enhance support for decision making. This work emphasizes the importance of acknowledging and discussing assumptions implicit to models.

Advancing Evaluation of Microplastics Thresholds to Inform Water Treatment Needs and Risks

Although human health impacts of microplastics are not well understood, concern regarding chemical contaminants retained on or within them is growing. Drinking water providers are increasingly asked about these risks, but strategies for evaluating them and the extent of treatment needed to manage them are currently lacking. Microplastics can potentially induce health effects if the concentration of contaminants adsorbed to them exceeds predetermined drinking water guidelines (e.g., Maximum Contaminant Levels). The risk posed by microplastics due to adsorbed contaminants is difficult to determine, but a worst-case scenario can be evaluated by using adsorption capacity. Here, a "Threshold Microplastics Concentration" (TMC) framework is developed to evaluate whether waterborne microplastic concentrations can potentially result in the intake of regulated contaminants on/in microplastics at levels of human health concern and identify treatment targets for managing associated health risk. Exceeding the TMC does not indicate an immediate health risk; it informs the need for detailed risk assessment or further treatment evaluation to ensure particle removal targets are achieved. Thus, the TMC concept and framework provide an updateable, science-based screening tool to determine if there is a need for detailed risk assessment or treatment modification due to waterborne microplastics in supplies used for potable water production.

Comparison of optical density-based growth kinetics for pure culture Campylobacter jejuni, coli and lari grown in blood-free Bolton broth

Campylobacter growth kinetic parameters can be used to refine the sensitivity and efficiency of microbial growth-based methods. Therefore, the aim of this study was to construct growth curves for C. jejuni, C. coli, and C. lari in pure culture and calculate growth kinetics for each Campylobacter species in the same environmental conditions. Campylobacter jejuni, C. coli and C. lari were grown over 48 h and inoculated into 15 mL Hungate tubes (N = 3 trials per species; 5 biological replicates per trial; 3 species; 1 strain per species). Absorbance measurements were taken in 45 min intervals over 24 h. Optical density readings were plotted versus time to calculate growth kinetic parameters. C. jejuni exhibited the longest lag phase (p < 0.001) at 15 h 20 min ± 30 min, versus C. coli at 11 h 15 min ± 17 min, and C. lari at 9 h 27 min ± 15 min. The exponential phase duration was no longer than 5 h for all species, and doubling times were all less than 1h 30 min. The variation in growth kinetics for the three species of Campylobacter illustrates the importance of determining individual Campylobacter spp. growth responses for optimizing detection based on low bacterial levels. This study provides kinetics and estimates to define enrichment times necessary for low concentration Campylobacter detection.

Realizing the value in "non-standard" parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology

The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain "non-standard" data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modeling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modeling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.

The Most Probable Curve method - A robust approach to estimate kinetic models from low plate count data resulting in reduced uncertainty

A novel method is proposed for fitting microbial inactivation models to data on liquid media: the Most Probable Curve (MPC) method. It is a multilevel model that makes a separation between the "true" microbial concentration according to the model, the "actual" concentration in the media considering chance, and the actual counts on the plate. It is based on the assumptions that stress resistance is homogeneous within a microbial population, and that there is no aggregation of microbial cells. Under these assumptions, the number of colonies in/on a plate follows a Poisson distribution with expected value depending on the proposed kinetic model, the number of dilutions and the plated volume. The novel method is compared against (non)linear regression based on a normal likelihood distribution (traditional method), Poisson regression and gamma-Poisson regression using data on the inactivation of Listeria monocytogenes. The conclusion is that the traditional method has limitations when the data includes plates with low (or zero) cell counts, which can be mitigated using more complex (discrete) likelihoods. However, Poisson regression uses an unrealistic likelihood function, making it unsuitable for survivor curves with several log-reductions. Gamma-Poisson regression uses a more realistic likelihood function, even though it is based mostly on empirical hypotheses. We conclude that the MPC method can be used reliably, especially when the data includes plates with low or zero counts. Furthermore, it generates a more realistic description of uncertainty, integrating the contribution of the plating error and reducing the uncertainty of the primary model parameters. Consequently, although it increases modelling complexity, the MPC method can be of great interest in predictive microbiology, especially in studies focused on variability analysis.

Ensuring That Fundamentals of Quantitative Microbiology Are Reflected in Microbial Diversity Analyses Based on Next-Generation Sequencing

Diversity analysis of amplicon sequencing data has mainly been limited to plug-in estimates calculated using normalized data to obtain a single value of an alpha diversity metric or a single point on a beta diversity ordination plot for each sample. As recognized for count data generated using classical microbiological methods, amplicon sequence read counts obtained from a sample are random data linked to source properties (e.g., proportional composition) by a probabilistic process. Thus, diversity analysis has focused on diversity exhibited in (normalized) samples rather than probabilistic inference about source diversity. This study applies fundamentals of statistical analysis for quantitative microbiology (e.g., microscopy, plating, and most probable number methods) to sample collection and processing procedures of amplicon sequencing methods to facilitate inference reflecting the probabilistic nature of such data and evaluation of uncertainty in diversity metrics. Following description of types of random error, mechanisms such as clustering of microorganisms in the source, differential analytical recovery during sample processing, and amplification are found to invalidate a multinomial relative abundance model. The zeros often abounding in amplicon sequencing data and their implications are addressed, and Bayesian analysis is applied to estimate the source Shannon index given unnormalized data (both simulated and experimental). Inference about source diversity is found to require knowledge of the exact number of unique variants in the source, which is practically unknowable due to library size limitations and the inability to differentiate zeros corresponding to variants that are actually absent in the source from zeros corresponding to variants that were merely not detected. Given these problems with estimation of diversity in the source even when the basic multinomial model is valid, diversity analysis at the level of samples with normalized library sizes is discussed.

Modeling Exposure to Fecal Contamination in Drinking Water due to Multiple Water Source Use

The Joint Monitoring Programme estimated that 71% of people globally had access to "safely managed" drinking water in 2017. However, typical data collection practices focus only on a household's primary water source, yet some households in low- and middle-income countries (LMICs) engage in multiple water source use, including supplementing improved water supplies with unimproved water throughout the year. Monte Carlo simulations and previously published data were used to simulate exposure to fecal contamination (as measured by E. coli) along a range of supplemental unimproved source use rates (e.g., 0-100% improved water use, with the remainder made up with unimproved water). The model results revealed a statistically significant increase in annual exposure to E. coli when individuals supplement their improved water with unimproved water just 2 days annually. Additionally, our analysis identified scenarios-realistic for the data set study setting-where supplementing with unimproved water counterintuitively decreases exposure to E. coli. These results highlight the need for evaluating the temporal dynamics in water quality and availability of drinking water sources in LMICs as well as capturing the use of multiple water sources for monitoring global access to safe drinking water.

Microbial-Maximum Likelihood Estimation Tool for Microbial Quantification in Food From Left-Censored Data Using Maximum Likelihood Estimation for Microbial Risk Assessment

In food microbial measurements, when most or very often bacterial counts are below to the limit of quantification (LOQ) or the limit of detection (LOD) in collected food samples, they are either ignored or a specified value is substituted. The consequence of this approach is that it may lead to the over or underestimation of quantitative results. A maximum likelihood estimation (MLE) or Bayesian models can be applied to deal with this kind of censored data. Recently, in food microbiology, an MLE that deals with censored results by fitting a parametric distribution has been introduced. However, the MLE approach has limited practical application in food microbiology as practical tools for implementing MLE statistical methods are limited. We therefore developed a user-friendly MLE tool (called “Microbial-MLE Tool”), which can be easily used without requiring complex mathematical knowledge of MLE but the tool is designated to adjust log-normal distributions to observed counts, and illustrated how this method may be implemented for food microbial censored data using an Excel spreadsheet. In addition, we used two case studies based on food microbial laboratory measurements to illustrate the use of the tool. We believe that the Microbial-MLE tool provides an accessible and comprehensible means for performing MLE in food microbiology and it will also be of help to improve the outcome of quantitative microbial risk assessment (MRA).

Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities

Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy. In water resources management, it can be especially useful to evaluate ecosystem shifts in response to natural and anthropogenic landscape disturbances to signal potential water quality concerns, such as the detection of toxic cyanobacteria or pathogenic bacteria. Amplicon sequencing data consist of discrete counts of sequence reads, the sum of which is the library size. Groups of samples typically have different library sizes that are not representative of biological variation; library size normalization is required to meaningfully compare diversity between them. Rarefaction is a widely used normalization technique that involves the random subsampling of sequences from the initial sample library to a selected normalized library size. This process is often dismissed as statistically invalid because subsampling effectively discards a portion of the observed sequences, yet it remains prevalent in practice and the suitability of rarefying, relative to many other normalization approaches, for diversity analysis has been argued. Here, repeated rarefying is proposed as a tool to normalize library sizes for diversity analyses. This enables (i) proportionate representation of all observed sequences and (ii) characterization of the random variation introduced to diversity analyses by rarefying to a smaller library size shared by all samples. While many deterministic data transformations are not tailored to produce equal library sizes, repeatedly rarefying reflects the probabilistic process by which amplicon sequencing data are obtained as a representation of the amplified source microbial community. Specifically, it evaluates which data might have been obtained if a particular sample's library size had been smaller and allows graphical representation of the effects of this library size normalization process upon diversity analysis results.

BIKE: Dietary Exposure Model for Foodborne Microbiological and Chemical Hazards

BIKE is a Bayesian dietary exposure assessment model for microbiological and chemical hazards. A graphical user interface was developed for running the model and inspecting the results. It is based on connected Bayesian hierarchical models, utilizing OpenBUGS and R in tandem. According to occurrence and consumption data given as inputs, a specific BUGS code is automatically written for running the Bayesian model in the background. The user interface is based on shiny app. Chronic and acute exposures are estimated for chemical and microbiological hazards, respectively. Uncertainty and variability in exposures are visualized, and a few optional model structures can be used. Simulated synthetic data are provided with BIKE for an example, resembling real occurrence and consumption data. BIKE is open source and available from github.

Importance of Distributional Forms for the Assessment of Protozoan Pathogens Concentrations in Drinking-Water Sources

The identification of appropriately conservative statistical distributions is needed to predict microbial peak events in drinking water sources explicitly. In this study, Poisson and mixed Poisson distributions with different upper tail behaviors were used for modeling source water Cryptosporidium and Giardia data from 30 drinking water treatment plants. Small differences (<0.5-log) were found between the "best" estimates of the mean Cryptosporidium and Giardia concentrations with the Poisson-gamma and Poisson-log-normal models. However, the upper bound of the 95% credibility interval on the mean Cryptosporidium concentrations of the Poisson-log-normal model was considerably higher (>0.5-log) than that of the Poisson-gamma model at four sites. The improper choice of a model may, therefore, mislead the assessment of treatment requirements and health risks associated with the water supply. Discrimination between models using the marginal deviance information criterion (mDIC) was unachievable because differences in upper tail behaviors were not well characterized with available data sets ( n<30 ). Therefore, the gamma and the log-normal distributions fit the data equally well but may predict different risk estimates when they are used as an input distribution in an exposure assessment. The collection of event-based monitoring data and the modeling of larger routine monitoring data sets are recommended to identify appropriately conservative distributions to predict microbial peak events.

Fecal Indicator Bacteria Data to Characterize Drinking Water Quality in Low-Resource Settings: Summary of Current Practices and Recommendations for Improving Validity

Fecal indicator bacteria (FIB) values are widely used to assess microbial contamination in drinking water and to advance the modeling of infectious disease risks. The membrane filtration (MF) testing technique for FIB is widely adapted for use in low- and middle-income countries (LMICs). We conducted a systematic literature review on the use of MF-based FIB data in LMICs and summarized statistical methods from 172 articles. We then applied the commonly used statistical methods from the review on publicly available datasets to illustrate how data analysis methods affect FIB results and interpretation. Our findings indicate that standard methods for processing samples are not widely reported, the selection of statistical tests is rarely justified, and, depending on the application, statistical methods can change risk perception and present misleading results. These results raise concerns about the validity of FIB data collection, analysis, and presentation in LMICs. To improve evidence quality, we propose a FIB data reporting checklist to use as a reminder for researchers and practitioners.

Evaluation of groundwater bacterial community composition to inform waterborne pathogen vulnerability assessments

Microbial water quality evaluations are essential for determining the vulnerability of subsurface drinking water sources to fecal pathogen intrusion. Rather than directly monitor waterborne pathogens using culture- or enumeration-based techniques, the potential of assessing bacterial community using 16S rRNA gene amplicon sequencing to support these evaluations was investigated. A framework for analyzing 16S rRNA gene amplicon sequencing results featuring negative-binomial generalized linear models is demonstrated, and applied to bacterial taxa sequences in purge water samples collected from a shallow, highly aerobic, unconfined aquifer. Bacterial taxa relevant as indicators of fecal source and surface connectivity were examined using this approach. Observed sequences of Escherichia, a genus suggestive of fecal source, were consistently detected but not confirmed by culture-based methods. On the other hand, episodic appearance of anaerobic taxa sequences in this highly aerobic environment, namely Clostridia and Bacteroides, warrants further investigation as potential indicators of fecal contamination. Betaproteobacteria sequences varied significantly on a seasonal basis, and therefore may be linked to understanding surface-water groundwater interactions at this site. However, sequences that are often encountered in surface water bodies (Cyanobacteria and Flavobacteriia) were notably absent or present at very low levels, suggesting that microbial transport from surface-derived sources may be rather limited. This work demonstrates the utility of 16S rRNA gene amplicon sequencing for contextualizing and complementing conventional microbial techniques, allowing for hypotheses about source and transport processes to be tested and refined.

Application of QMRA to MAR operations for safe agricultural water reuses in coastal areas

A pathogenic Escherichia coli (E.coli) O157:H7 and O26:H11 dose-response model was set up for a quantitative microbial risk assessment (QMRA) of the waterborne diseases associated with managed aquifer recharge (MAR) practices in semiarid regions. The MAR facility at Forcatella (Southern Italy) was selected for the QMRA application. The target counts of pathogens incidentally exposed to hosts by eating contaminated raw crops or while bathing at beaches of the coastal area were determined by applying the Monte Carlo Markov Chain (MCMC) Bayesian method to the water sampling results. The MCMC provided the most probable pathogen count reaching the target and allowed for the minimization of the number of water samplings, and hence, reducing the associated costs. The sampling stations along the coast were positioned based on the results of a groundwater flow and pathogen transport model, which highlighted the preferential flow pathways of the transported E. coli in the fractured coastal aquifer. QMRA indicated tolerable (<10^-6 DALY) health risks for bathing at beaches and irrigation with wastewater, with 0.4 infectious diseases per year (11.4% probability of occurrence) associated with the reuse of reclaimed water via soil irrigation even though exceeding the E. coli regulation limit of 10 CFU/100 mL by five times. The results show negligible health risk and insignificant impacts on the coastal water quality due to pathogenic E. coli in the wastewater used for MAR. However, droughts and reclaimed water quality can be considered the main issues of MAR practices in semiarid regions suggesting additional reclaimed water treatments and further stress-tests via QMRAs by considering more persistent pathogens than E. coli.

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.

Guidelines for the design of (optimal) isothermal inactivation experiments

Kinetic models are nowadays a basic tool to ensure food safety. Most models used in predictive microbiology have model parameters, whose precision is crucial to provide meaningful predictions. Kinetic parameters are usually estimated based on experimental data, where the experimental design can have a great impact on the precision of the estimates. In this sense, Optimal Experiment Design (OED) applies tools from optimization and information theory to identify the most informative experiment under a set of constrains (e.g. mathematical model, number of samples, etc). In this work, we develop a methodology for the design of optimal isothermal inactivation experiments. We consider the two dimensions of the design space (time and temperature), as well as a temperature-dependent maximum duration of the experiment. Functions for its application have been included in the bioOED R package. We identify design patterns that remain optimum regardless of the number of sampling points for three inactivation models (Bigelow, Mafart and Peleg) and three model microorganisms (Escherichia coli, Salmonella Senftenberg and Bacillus coagulans). Samples at extreme temperatures and close to the maximum duration of the experiment are the most informative. Moreover, the Mafart and Peleg models require some samples at intermediate time points due to the non-linearity of the survivor curve. The impact of the reference temperature on the precision of the parameter estimates is also analysed. Based on numerical simulations we recommend fixing it to the mean of the maximum and minimum temperatures used for the experiments. The article ends with a discussion presenting guidelines for the design of isothermal inactivation experiments. They combine these optimum results based on information theory with several practical limitations related to isothermal inactivation experiments. The application of these guidelines would reduce the experimental burden required to characterize thermal inactivation.

Enteric viruses in municipal wastewater effluent before and after disinfection with chlorine and ultraviolet light

In Ontario, Canada, information is lacking on chlorine and ultraviolet (UV) light disinfection performance against enteric viruses in wastewater. We enumerated enteroviruses and noroviruses, coliphages, and Escherichia coli per USEPA methods 1615, 1602, and membrane filtration, respectively, in pre- and post-disinfection effluent at five wastewater treatment plants (WWTPs), with full-year monthly sampling, and calculated log₁₀ reductions (LRs) while WWTPs complied with their monthly geometric mean limit of 200 E. coli/100 mL. Modeling of densities by left-censored estimation and Bayesian inference gave very similar results. Polymerase chain reaction (PCR)-detected enteroviruses and noroviruses were abundant in post-disinfection effluent (mean concentrations of 2.1 × 10⁺⁴-7.2 × 10⁺⁵ and 2.7 × 10⁺⁴-3.6 × 10⁺⁵ gene copies (GC)/L, respectively). Chlorine or UV disinfection produced modest LRs for culture- (0.3-0.9) and PCR-detected enteroviruses (0.3-1.3), as well as noroviruses GI + GII (0.5-0.8). Coliphages and E. coli were more susceptible, with LRs of 0.8-3.0 and 2.5, respectively. Sand-filtered effluent produced significantly higher enteric virus LRs (except cultured enteroviruses). Coliphage and human enteric virus densities gave significantly positive correlations using Kendall's Tau test. Enteric viruses are abundant in wastewater effluent following routine chlorine or UV disinfection processes that target E. coli. Coliphages appear to be good indicators for evaluating wastewater disinfection of enteric viruses.

Identification of eyeworm (Oxyspirura petrowi) and caecal worm (Aulonocephalus pennula) infection levels in Northern bobwhite quail (Colinus virginianus) of the Rolling Plains, TX using a mobile research laboratory: Implications for regional surveillance

Over the last few decades, there has been a decline in Northern bobwhite quail (Colinus virginianus) throughout their native range. While there are various factors that may be influencing this decline, it is suggested that parasites should be taken into consideration as a potential contributor in the Rolling Plains Ecoregion. High prevalence of the eyeworm (Oxyspirura petrowi) and caecal worm (Aulonocephalus pennula) in bobwhite of this region, coupled with a continuous decline, creates a need to assess infection through alternative methods for regional surveillance. Previous studies have developed a qPCR method and mobile research laboratory as an option for nonlethal procedures. However, there is still a need for standardization of these techniques. Therefore, this study builds on previous protocols to develop an application that considers factors that may influence qPCR results. In this study, cloacal swabs are collected from bobwhite in three locations throughout the Rolling Plains and scaled based on amount of feces present on the swab. This data is compared to qPCR standards as a limit of quantification for both eyeworm and caecal worm to define parasitic infection levels. Binary logistic regressions confirm that the probability of detection increases for both eyeworm (Odds Ratio: 2.3738; 95% Confidence Interval: [1.7804, 3.1649]) and caecal worm (Odds Ratio: 2.8516; 95% Confidence Interval: [2.2235, 3.6570]) as swab score increases. Infection levels for eyeworm and caecal worm are based on the generated cycle threshold value averages of qPCR standards. Based on the results of this study, this method can be applied in the mobile research laboratory to quantitatively assess regional parasitic infection in bobwhite throughout the Rolling Plains.