Delta SARS-CoV-2 variant is entirely substituted by the omicron variant during the fifth COVID-19 wave in Attica region.

Wastewater analysis is the most attractive alternative way for the quantification and variant profiling of SARS-CoV-2. Infection dynamics can be monitored by RT-qPCR assays while NGS can provide evidence for the presence of existing or new emerging SARS-CoV-2 variants. Herein, apart from the infection dynamic in Attica since June 1st, 2021, the monitoring of 9 mutations of the omicron and 4 mutations of the delta SARS-CoV-2 variants, utilizing both novel Nested-Seq and RT-PCR, is reported and the substitution of the delta variant (B.1.617.2) by the omicron variant (B.1.1.529) in Attica, Greece within approximately one month is highlighted. The key difference between the two methodologies is discovery power. RT-PCR can only detect known sequences cost-effectively, while NGS is a hypothesis-free approach that does not require prior knowledge to detect novel genes. Overall, the potential of wastewater genomic surveillance for the early discovery and monitoring of variants important for disease management at the community level is underlined. This is the first study, reporting the SARS-CoV-2 infection dynamic for an extended time period and the first attempt to monitor two of the most severe variants with two different methodologies in Greece.

Evaluation of viral concentration and extraction methods for SARS-CoV-2 recovery from wastewater using droplet digital and quantitative RT-PCR.

The ongoing pandemic caused by the emergence of SARS-CoV-2 has resulted in millions of deaths worldwide despite the various measures announced by the authorities. Wastewater-based epidemiology has the ability to provide a day-to-day estimation of the number of infected people in a fast and cost-effective manner. However, owing to the complex nature of wastewater, wastewater monitoring for viral genome copies is affected by the extensive viral fragmentation that takes place all the way to the sewage and the analytical lab. The aim of this study was to evaluate different methodologies for the concentration and extraction of viruses in wastewaters and to select and improve an option that maximizes the recovery of SARS-CoV-2. We compare 5 different concentration methods and 4 commercially available kits for the RNA extraction. To evaluate the performance and the recovery of these, SARS-CoV-2 isolated from patients was used as a spike control. Additionally, the presence of SARS-CoV-2 in all wastewater samples was determined using reverse transcription quantitative PCR (RT-qPCR) and reverse transcription droplet digital PCR (RT-ddPCR), targeting three genetic markers (N1, N2 and N3). Using spiked samples, recoveries were estimated 2.1-37.6% using different extraction kits and 0.1-2.1% using different concentration kits. It was found that a direct capture-based method, evaluated against a variety of concentration methods, is the best in terms of recovery, time and cost. Interestingly, we noticed a good agreement between the results provided by RT-qPCR and RT-ddPCR in terms of recovery. This evaluation can serve as a guide for laboratories establishing a protocol to perform wastewater monitoring of SARS-CoV-2. Overall, data presented here reinforces the validity of WBE for SARS-CoV-2 surveillance, uncovers potential caveats in the selection of concentration and extraction protocols and points towards optimal solutions to maximize its potential.

SARS-CoV-2 wastewater surveillance data can predict hospitalizations and ICU admissions.

We measured SARS-CoV-2 RNA load in raw wastewater in Attica, Greece, by RT-qPCR for the environmental surveillance of COVID-19 for 6 months. The lag between RNA load and pandemic indicators (COVID-19 hospital and intensive care unit (ICU) admissions) was calculated using a grid search. Our results showed that RNA load in raw wastewater is a leading indicator of positive COVID-19 cases, new hospitalization and admission into ICUs by 5, 8 and 9 days, respectively. Modelling techniques based on distributed/fixed lag modelling, linear regression and artificial neural networks were utilized to build relationships between SARS-CoV-2 RNA load in wastewater and pandemic health indicators. SARS-CoV-2 mutation analysis in wastewater during the third pandemic wave revealed that the alpha-variant was dominant. Our results demonstrate that clinical and environmental surveillance data can be combined to create robust models to study the on-going COVID-19 infection dynamics and provide an early warning for increased hospital admissions.