Statistical Approaches for Modeling in Microbial Source Tracking

Belanche, Lluís; Blanch, Anicet R.

doi:10.1007/978-1-4419-9386-1_9

Cited by 5 publications

(14 citation statements)

References 48 publications

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“…21 Models incorporating the uncertainties arising from low source-specificity can be used to assess whether a reliable source identification, or even quantitative allocation, can been achieved. 3,10,20−22 …”

Section: Discussionmentioning

confidence: 99%

Performance Characteristics of qPCR Assays Targeting Human- and Ruminant-AssociatedBacteroidetesfor Microbial Source Tracking across Sixteen Countries on Six Continents

Reischer¹,

Ebdon²,

Bauer³

et al. 2013

Environ. Sci. Technol.

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Numerous quantitative PCR assays for microbial fecal source tracking (MST) have been developed and evaluated in recent years. Widespread application has been hindered by a lack of knowledge regarding the geographical stability and hence applicability of such methods beyond the regional level. This study assessed the performance of five previously reported quantitative PCR assays targeting human-, cattle-, or ruminant-associated Bacteroidetes populations on 280 human and animal fecal samples from 16 countries across six continents. The tested cattle-associated markers were shown to be ruminant-associated. The quantitative distributions of marker concentrations in target and nontarget samples proved to be essential for the assessment of assay performance and were used to establish a new metric for quantitative source-specificity. In general, this study demonstrates that stable target populations required for marker-based MST occur around the globe. Ruminant-associated marker concentrations were strongly correlated with total intestinal Bacteroidetes populations and with each other, indicating that the detected ruminant-associated populations seem to be part of the intestinal core microbiome of ruminants worldwide. Consequently tested ruminant-targeted assays appear to be suitable quantitative MST tools beyond the regional level while the targeted human-associated populations seem to be less prevalent and stable, suggesting potential for improvements in human-targeted methods.

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Section: Discussionmentioning

confidence: 99%

Performance Characteristics of qPCR Assays Targeting Human- and Ruminant-AssociatedBacteroidetesfor Microbial Source Tracking across Sixteen Countries on Six Continents

Reischer¹,

Ebdon²,

Bauer³

et al. 2013

Environ. Sci. Technol.

Self Cite

115

151

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“…It can also be questioned to what extent the MST assays improve the knowledge on fecal sources obtained by traditional methods. Different modeling approaches have been used in MST, some of which include Bayesian procedures and algorithmic methods . In the Bayesian approach presented here, subjective information from expert judgments was incorporated in the prior.…”

Section: Discussionmentioning

confidence: 99%

“…In previous MST studies, statistical and geographical analyses have been performed to assess the relationship between fecal sources and host-specific markers in source waters , and it was recommended to combine different assays to strengthen the MST analyses. , Several modeling techniques have been applied in MST, entailing different working assumptions and results . Logistic regression (LR) can be used to model relationships between predictor variables and response variables that are not normally distributed.…”

Section: Discussionmentioning

confidence: 99%

“…The Bayesian approach presented here is a structured way to incorporate knowledge on fecal sources and FIO data in the prior, which is updated with information on the assay performance. In comparison to alternative modeling techniques the approach is associated with low cost and ease of deployment to different geographic regions, which is a requested feature …”

Section: Discussionmentioning

confidence: 99%

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Incorporating Expert Judgments in Utility Evaluation of Bacteroidales qPCR Assays for Microbial Source Tracking in a Drinking Water Source

Åström

Pettersson²,

Reischer

et al. 2015

Environ. Sci. Technol.

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Several assays for the detection of host-specific genetic markers of the order Bacteroidales have been developed and used for microbial source tracking (MST) in environmental waters. It is recognized that the source-sensitivity and source-specificity are unknown and variable when introducing these assays in new geographic regions, which reduces their reliability and use. A Bayesian approach was developed to incorporate expert judgments with regional assay sensitivity and specificity assessments in a utility evaluation of a human and a ruminant-specific qPCR assay for MST in a drinking water source. Water samples from Lake Rådasjön were analyzed for E. coli, intestinal enterococci and somatic coliphages through cultivation and for human (BacH) and ruminant-specific (BacR) markers through qPCR assays. Expert judgments were collected regarding the probability of human and ruminant fecal contamination based on fecal indicator organism data and subjective information. Using Bayes formula, the conditional probability of a true human or ruminant fecal contamination given the presence of BacH or BacR was determined stochastically from expert judgments and regional qPCR assay performance, using Beta distributions to represent uncertainties. A web-based computational tool was developed for the procedure, which provides a measure of confidence to findings of host-specific markers and demonstrates the information value from these assays.

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“…To date, it is still difficult to determine optimal models and appropriate variables for predicting the sources of microbial contamination ( Belanche-Muñoz and Blanch, 2008 ; Belanche and Blanch, 2011 ). This is due in part to the complexity of host-specific sources of microbial contaminants in water, which may originate from humans, birds, dogs, or other animals.…”

Section: Introductionmentioning

confidence: 99%

Tracking Major Sources of Water Contamination Using Machine Learning

Song

Dubinsky

et al. 2021

Front. Microbiol.

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Current microbial source tracking techniques that rely on grab samples analyzed by individual endpoint assays are inadequate to explain microbial sources across space and time. Modeling and predicting host sources of microbial contamination could add a useful tool for watershed management. In this study, we tested and evaluated machine learning models to predict the major sources of microbial contamination in a watershed. We examined the relationship between microbial sources, land cover, weather, and hydrologic variables in a watershed in Northern California, United States. Six models, including K-nearest neighbors (KNN), Naïve Bayes, Support vector machine (SVM), simple neural network (NN), Random Forest, and XGBoost, were built to predict major microbial sources using land cover, weather and hydrologic variables. The results showed that these models successfully predicted microbial sources classified into two categories (human and non-human), with the average accuracy ranging from 69% (Naïve Bayes) to 88% (XGBoost). The area under curve (AUC) of the receiver operating characteristic (ROC) illustrated XGBoost had the best performance (average AUC = 0.88), followed by Random Forest (average AUC = 0.84), and KNN (average AUC = 0.74). The importance index obtained from Random Forest indicated that precipitation and temperature were the two most important factors to predict the dominant microbial source. These results suggest that machine learning models, particularly XGBoost, can predict the dominant sources of microbial contamination based on the relationship of microbial contaminants with daily weather and land cover, providing a powerful tool to understand microbial sources in water.

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Statistical Approaches for Modeling in Microbial Source Tracking

Cited by 5 publications

References 48 publications

Performance Characteristics of qPCR Assays Targeting Human- and Ruminant-AssociatedBacteroidetesfor Microbial Source Tracking across Sixteen Countries on Six Continents

Performance Characteristics of qPCR Assays Targeting Human- and Ruminant-AssociatedBacteroidetesfor Microbial Source Tracking across Sixteen Countries on Six Continents

Incorporating Expert Judgments in Utility Evaluation of Bacteroidales qPCR Assays for Microbial Source Tracking in a Drinking Water Source

Tracking Major Sources of Water Contamination Using Machine Learning

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