Wastewater monitoring of SARS-CoV-2 shows high correlation with COVID-19 case numbers and allowed early detection of the first confirmed B.1.1.529 infection in Switzerland: results of an observational surveillance study.

AIMS OF THE STUDY: Wastewater-based epidemiology has contributed significantly to the comprehension of the dynamics of the current COVID-19 pandemic. Its additional value in monitoring SARS-CoV-2 circulation in the population and identifying newly arising variants independently of diagnostic testing is now undisputed. As a proof of concept, we report here correlations between SARS-CoV-2 detection in wastewater and the officially recorded COVID-19 case numbers, as well as the validity of such surveillance to detect emerging variants, exemplified by the detection of the B.1.1.529 variant Omicron in Basel, Switzerland. METHODS: From July 1 to December 31, 2021, wastewater samples were collected six times a week from the inflow of the local wastewater treatment plant that receives wastewater from the catchment area of the city of Basel, Switzerland, comprising 273,075 inhabitants. The number of SARS-CoV-2 RNA copies was determined by reverse transcriptase-quantitative PCR. Spearman's rank correlation coefficients were calculated to determine correlations with the median seven-day incidence of genome copies per litre of wastewater and official case data. To explore delayed correlation effects between the seven-day median number of genome copies/litre wastewater and the median seven-day incidence of SARS-CoV-2 cases, time-lagged Spearman's rank correlation coefficients were calculated for up to 14 days. RNA extracts from daily wastewater samples were used to genotype circulating SARS-CoV-2 variants by next-generation sequencing. RESULTS: The number of daily cases and the median seven-day incidence of SARS-CoV-2 infections in the catchment area showed a high correlation with SARS-CoV-2 measurements in wastewater samples. All correlations between the seven-day median number of genome copies/litre wastewater and the time-lagged median seven-day incidence of SARS-CoV-2 cases were significant (p<0.001) for the investigated lag of up to 14 days. Correlation coefficients declined constantly from the maximum of 0.9395 on day 1 to the minimum of 0.8016 on day 14. The B.1.1.529 variant Omicron was detected in wastewater samples collected on November 21, 2021, before its official acknowledgement in a clinical sample by health authorities. CONCLUSIONS: In this proof-of-concept study, wastewater-based epidemiology proved a reliable and sensitive surveillance approach, complementing routine clinical testing for mapping COVID-19 pandemic dynamics and observing newly circulating SARS-CoV-2 variants.

Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections.

Detection of incident hepatitis C virus (HCV) infections is crucial for identification of outbreaks and development of public health interventions. However, there is no single diagnostic assay for distinguishing recent and persistent HCV infections. HCV exists in each infected host as a heterogeneous population of genomic variants, whose evolutionary dynamics remain incompletely understood. Genetic analysis of such viral populations can be applied to the detection of incident HCV infections and used to understand intra-host viral evolution. We studied intra-host HCV populations sampled using next-generation sequencing from 98 recently and 256 persistently infected individuals. Genetic structure of the populations was evaluated using 245,878 viral sequences from these individuals and a set of selected features measuring their diversity, topological structure, complexity, strength of selection, epistasis, evolutionary dynamics, and physico-chemical properties. Distributions of the viral population features differ significantly between recent and persistent infections. A general increase in viral genetic diversity from recent to persistent infections is frequently accompanied by decline in genomic complexity and increase in structuredness of the HCV population, likely reflecting a high level of intra-host adaptation at later stages of infection. Using these findings, we developed a machine learning classifier for the infection staging, which yielded a detection accuracy of 95.22 per cent, thus providing a higher accuracy than other genomic-based models. The detection of a strong association between several HCV genetic factors and stages of infection suggests that intra-host HCV population develops in a complex but regular and predictable manner in the course of infection. The proposed models may serve as a foundation of cyber-molecular assays for staging infection, which could potentially complement and/or substitute standard laboratory assays.