Discrimination and surveillance of infectious severe acute respiratory syndrome Coronavirus 2 in wastewater using cell culture and RT-qPCR.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA has been extensively detected in raw wastewater in studies exploring wastewater-based epidemiology (WBE) for early warning purposes. Nonetheless, only a few limited studies investigated the presence of SARS-CoV-2 in treated wastewaters to determine the potential health risks across the water cycle. The detection of SARS-CoV-2 has been done mostly by RT-qPCR and ddPCR, which only provides information on the presence of nucleic acids rather than information on potential infectivity. In this study, we set to develop and evaluate the use of viability RT-qPCR for the selective discrimination and surveillance of infectious SARS-CoV-2 in secondary-treated wastewater. Enzymatic (nuclease) and viability dye (Reagent D) pretreatments were applied to infer infectivity through RT-qPCR using porcine epidemic diarrhea virus (PEDV) as a CoV surrogate. Infectivity tests were first performed on PEDV purified RNA, then on infectious and heat-inactivated PEDV, and finally on heat inactivated PEDV spiked in concentrated secondary-treated wastewater. The two viability RT-qPCR methods were then applied to 27 secondary-treated wastewater samples positive for SARS-CoV-2 RNA at the outlet of five large urban wastewater treatment plants in Portugal. Reagent D pretreatment showed similar behavior to cell culture for heat-inactivated PEDV and both viability RT-qPCR methods performed comparably to VERO E6 cell culture for SARS-CoV-2 present in secondary-treated wastewater, eliminating completely the RT-qPCR signal. Our study demonstrated the lack of infectious SARS-CoV-2 viral particles on secondary-treated wastewater through the application of two pretreatment methods for the rapid inference of infectivity through RT-qPCR, showing their potential application in environmental screening. This study addressed a knowledge gap on the public health risks of SARS-CoV-2 across the water cycle.

Recovery of SARS-CoV-2 from large volumes of raw wastewater is enhanced with the inuvai R180 system.

Wastewater-based epidemiology (WBE) for severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is a powerful tool to complement syndromic surveillance. Although detection of SARS-CoV-2 in raw wastewater may be prompted with good recoveries during periods of high community prevalence, in the early stages of population outbreaks concentration procedures are required to overcome low viral concentrations. Several methods have become available for the recovery of SARS-CoV-2 from raw wastewater, generally involving filtration. However, these methods are limited to small sample volumes, possibly missing the early stages of virus circulation, and restrained applicability across different water matrices. The aim of this study was thus to evaluate the performance of three methods enabling the concentration of SARS-CoV-2 from large volumes of wastewater: i) hollow fiber filtration using the inuvai R180, with an enhanced elution protocol and polyethylene glycol (PEG) precipitation; ii) PEG precipitation; and iii) skimmed milk flocculation. The performance of the three approaches was evaluated in wastewater from multiple wastewater treatment plants (WWTP) with distinct singularities, according to: i) effective volume; ii) percentage of recovery; iii) extraction efficiency; iv) inhibitory effect; and v) the limits of detection and quantification. The inuvai R180 system had the best performance, with detection of spiked control across all samples, with average recovery percentages of 68% for porcine epidemic diarrhea virus (PEDV), with low variability. Mean recoveries for PEG precipitation and skimmed milk flocculation were 9% and 14%, respectively. The inuvai R180 enables the scalability of volumes without negative impact on the costs, time for analysis, and recovery/inhibition. Moreover, hollow fiber ultrafilters favor the concentration of different microbial taxonomic groups. Such combined features make this technology attractive for usage in environmental waters monitoring.

A wastewater-based epidemiology tool for COVID-19 surveillance in Portugal.

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater produced interest in its use for sentinel surveillance at a community level and as a complementary approach to syndromic surveillance. With this work, we set the foundations for wastewater-based epidemiology (WBE) in Portugal by monitoring the trends of SARS-CoV-2 RNA circulation in the community, on a nationwide perspective during different epidemiological phases of the pandemic. The Charité assays (E_Sarbecco, RdRP, and N_Sarbecco) were applied to monitor, over 32-weeks (April to December 2020), the dynamics of SARS-CoV-2 RNA at the inlet of five wastewater treatment plants (WWTP), which together serve more than two million people in Portugal. Raw wastewater from three Coronavirus disease 2019 (COVID-19) reference hospitals was also analyzed during this period. In total, more than 600 samples were tested. For the first weeks, detection of SARS-CoV-2 RNA was sporadic, with concentrations varying from 103 to 105 genome copies per liter (GC/L). Prevalence of SARS-CoV-2 RNA increased steeply by the end of May into late June, mainly in Lisboa e Vale do Tejo region (LVT), during the reopening phase. After the summer, with the reopening of schools in mid-September and return to partial face-to-face work, a pronounced increase of SARS-CoV-2 RNA in wastewater was detected. In the LVT area, SARS-CoV-2 RNA load agreed with reported trends in hotspots of infection. Synchrony between trends of SARS-CoV-2 RNA in raw wastewater and daily new COVID-19 cases highlights the value of WBE as a surveillance tool, particularly after the phasing out of the epidemiological curve and when hotspots of disease re-emerge in the population which might be difficult to spot based solely on syndromic surveillance and contact tracing. This is the first study crossing several epidemiological stages highlighting the long-term use of WBE for SARS-CoV-2.

COVID-19: Nothing is Normal in this Pandemic.

This manuscript brings attention to inaccurate epidemiological concepts that emerged during the COVID-19 pandemic. In social media and scientific journals, some wrong references were given to a "normal epidemic curve" and also to a "log-normal curve/distribution". For many years, textbooks and courses of reputable institutions and scientific journals have disseminated misleading concepts. For example, calling histogram to plots of epidemic curves or using epidemic data to introduce the concept of a Gaussian distribution, ignoring its temporal indexing. Although an epidemic curve may look like a Gaussian curve and be eventually modelled by a Gauss function, it is not a normal distribution or a log-normal, as some authors claim. A pandemic produces highly-complex data and to tackle it effectively statistical and mathematical modelling need to go beyond the "one-size-fits-all solution". Classical textbooks need to be updated since pandemics happen and epidemiology needs to provide reliable information to policy recommendations and actions.