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.
Bayesian Estimation of Survivor Function for Censored Data Using Lognormal Mixture Distributions
