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).

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Methods for Handling Left-Censored Data in Quantitative Microbial Risk Assessment

Applied and Environmental Microbiology ◽

10.1128/aem.01203-18 ◽

2018 ◽

Vol 84 (20) ◽

Cited By ~ 10

Author(s):

Robert A. Canales ◽

Amanda M. Wilson ◽

Jennifer I. Pearce-Walker ◽

Marc P. Verhougstraete ◽

Kelly A. Reynolds

Keyword(s):

Risk Assessment ◽

Censored Data ◽

Limit Of Detection ◽

Environmental Microbiology ◽

Infection Risk ◽

Data Sets ◽

Quantitative Microbial Risk Assessment ◽

Data Set ◽

Microbial Risk Assessment ◽

Microbial Risk

ABSTRACTData below detection limits, left-censored data, are common in environmental microbiology, and decisions in handling censored data may have implications for quantitative microbial risk assessment (QMRA). In this paper, we utilize simulated data sets informed by real-world enterovirus water data to evaluate methods for handling left-censored data. Data sets were simulated with four censoring degrees (low [10%], medium [35%], high [65%], and severe [90%]) and one real-life censoring example (97%) and were informed by enterovirus data assuming a lognormal distribution with a limit of detection (LOD) of 2.3 genome copies/liter. For each data set, five methods for handling left-censored data were applied: (i) substitution with LOD/√2, (ii) lognormal maximum likelihood estimation (MLE) to estimate mean and standard deviation, (iii) Kaplan-Meier estimation (KM), (iv) imputation method using MLE to estimate distribution parameters (MI method 1), and (v) imputation from a uniform distribution (MI method 2). Each data set mean was used to estimate enterovirus dose and infection risk. Root mean square error (RMSE) and bias were used to compare estimated and known doses and infection risks. MI method 1 resulted in the lowest dose and infection risk RMSE and bias ranges for most censoring degrees, predicting infection risks at most 1.17 × 10−2from known values under 97% censoring. MI method 2 was the next overall best method. For medium to severe censoring, MI method 1 may result in the least error. If unsure of the distribution, MI method 2 may be a preferred method to avoid distribution misspecification.IMPORTANCEThis study evaluates methods for handling data with low (10%) to severe (90%) left-censoring within an environmental microbiology context and demonstrates that some of these methods may be appropriate when using data containing concentrations below a limit of detection to estimate infection risks. Additionally, this study uses a skewed data set, which is an issue typically faced by environmental microbiologists.

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Sapovirus in Wastewater Treatment Plants in Tunisia: Prevalence, Removal, and Genetic Characterization

Applied and Environmental Microbiology ◽

10.1128/aem.02093-17 ◽

2018 ◽

Vol 84 (6) ◽

pp. e02093-17 ◽

Cited By ~ 11

Author(s):

Miguel F. Varela ◽

Imen Ouardani ◽

Tsuyoshi Kato ◽

Syunsuke Kadoya ◽

Mahjoub Aouni ◽

...

Keyword(s):

Risk Assessment ◽

Wastewater Treatment ◽

Bayesian Estimation ◽

Quantitative Pcr ◽

Censored Data ◽

Wastewater Treatment Plants ◽

Quantitative Microbial Risk Assessment ◽

Content Type ◽

Microbial Risk Assessment ◽

Microbial Risk

ABSTRACTSapovirus(SaV), from theCaliciviridaefamily, is a genus of enteric viruses that cause acute gastroenteritis. SaV is shed at high concentrations with feces into wastewater, which is usually discharged into aquatic environments or reused for irrigation without efficient treatments. This study analyzed the incidence of human SaV in four wastewater treatment plants from Tunisia during a period of 13 months (December 2009 to December 2010). Detection and quantification were carried out using reverse transcription-quantitative PCR (RT-qPCR) methods, obtaining a prevalence of 39.9% (87/218). Sixty-one positive samples were detected in untreated water and 26 positive samples in processed water. The Dekhila plant presented the highest contamination levels, with a 63.0% prevalence. A dominance of genotype I.2 was observed on 15 of the 24 positive samples that were genetically characterized. By a Bayesian estimation algorithm, the SaV density in wastewater was estimated using left-censored data sets. The mean value of log SaV concentration in untreated wastewater ranged between 2.7 and 4.5 logs. A virus removal efficiency of 0.2 log was calculated for the Dekhila plant as the log ratio posterior distributions between untreated and treated wastewater. Multiple quantitative values obtained in this study must be available in quantitative microbial risk assessment in Tunisia as parameter values reflecting local conditions.IMPORTANCEHuman sapovirus (SaV) is becoming more prevalent worldwide and organisms in this genus are recognized as emerging pathogens associated with human gastroenteritis. The present study describes novel findings on the prevalence, seasonality, and genotype distribution of SaV in Tunisia and Northern Africa. In addition, a statistical approximation using Bayesian estimation of the posterior predictive distribution (“left-censored” data) was employed to solve methodological problems related with the limit of quantification of the quantitative PCR (qPCR). This approach would be helpful for the future development of quantitative microbial risk assessment procedures for wastewater.

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