Enhanced decay of coronaviruses in sewers with domestic wastewater.

Recent outbreaks caused by coronaviruses and their supposed potential fecal-oral transmission highlight the need for understanding the survival of infectious coronavirus in domestic sewers. To date, the survivability and decay of coronaviruses were predominately studied using small volumes of wastewater (normally 5-30 mL) in vials (in-vial tests). However, real sewers are more complicated than bulk wastewater (wastewater matrix only), in particular the presence of sewer biofilms and different operational conditions. This study investigated the decay of infectious human coronavirus 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), two typical surrogate coronaviruses, in laboratory-scale reactors mimicking the gravity (GS, gravity-driven sewers) and rising main sewers (RM, pressurized sewers) with and without sewer biofilms. The in-sewer decay of both coronaviruses was greatly enhanced in comparison to those reported in bulk wastewater through in-vial tests. 99% of HCoV-229E and FIPV decayed within 2 h under either GS or RM conditions with biofilms, in contrast to 6-10 h without biofilms. There is limited difference in the decay of HCoV and FIPV in reactors operated as RM or GS, with the T90 and T99 difference of 7-10 min and 14-20 min, respectively. The decay of both coronaviruses in sewer biofilm reactors can be simulated by biphasic first-order kinetic models, with the first-order rate constant 2-4 times higher during the first phase than the second phase. The decay of infectious HCoV and FIPV was significantly faster in the reactors with sewer biofilms than in the reactors without biofilms, suggesting an enhanced decay of these surrogate viruses due to the presence of biofilms and related processes. The mechanism of biofilms in virus adsorption and potential inactivation remains unclear and requires future investigations. The results indicate that the survivability of infectious coronaviruses detected using bulk wastewater overestimated the infectivity risk of coronavirus during wastewater transportations in sewers or the downstream treatment.

Analytical performance comparison of four SARS-CoV-2 RT-qPCR primer-probe sets for wastewater samples.

Current studies have confirmed the feasibility of SARS-CoV-2 RNA detection by RT-qPCR assays in wastewater samples as an effective surveillance tool of COVID-19 prevalence in a community. Analytical performance of various RT-qPCR assays has been compared against wastewater samples based on the positive ratio. However, there is no systematic comparison work has been conducted for both analytical sensitivity and quantitative reliability against wastewater, which are essential factors for WBE. In this study, the detection performance of four RT-qPCR primer-probe sets, including CCDC-N, CDC-N1, N-Sarbeco, and E-Sarbeco, was systematically evaluated with pure synthetized plasmids, spiked wastewater mocks and raw wastewater samples. In addition to confirm RT-qPCR results, Nanopore sequencing was employed to delineate at molecular level for the analytical sensitivity and reproducibility of those primer-probe sets. CCDC-N showed high sensitivity and the broadest linearity range for wastewater samples. It was thus recommended to be the most efficient tool in the quantitative analysis of SARS-CoV-2 in wastewater. CDC-N1 had the highest sensitivity for real wastewater and thus would be suitable for the screening of wastewater for the presence of SARS-CoV-2. When applying the primer-probe sets to wastewater samples collected from different Australian catchments, increased active clinical cases were observed with the augment of SARS-CoV-2 RNA quantified by RT-qPCR in wastewater in low prevalence communities.

Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has been regarded as a potential tool for the prevalence estimation of coronavirus disease 2019 (COVID-19) in the community. However, the application of the conventional back-estimation approach is currently limited due to the methodological challenges and various uncertainties. This study systematically performed meta-analysis for WBE datasets and investigated the use of data-driven models for the COVID-19 community prevalence in lieu of the conventional WBE back-estimation approach. Three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were applied to the multi-national WBE dataset. To evaluate the robustness of these models, predictions for sixteen scenarios with partial inputs were compared against the actual prevalence reports from clinical testing. The performance of models was further validated using unseen data (data sets not included for establishing the model) from different stages of the COVID-19 outbreak. Generally, ANN and ANFIS models showed better accuracy and robustness over MLR models. Air and wastewater temperature played a critical role in the prevalence estimation by data-driven models, especially MLR models. With unseen datasets, ANN model reasonably estimated the prevalence of COVID-19 (cumulative cases) at the initial phase and forecasted the upcoming new cases in 2-4 days at the post-peak phase of the COVID-19 outbreak. This study provided essential information about the feasibility and accuracy of data-driven estimation of COVID-19 prevalence through the WBE approach.