Background
Wastewater-based epidemiology (WBE) for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be an important source of information for coronavirus disease 2019 (COVID-19) management during and after the pandemic. Currently, governments and transportation industries around the world are developing strategies to minimise SARS-CoV-2 transmission associated with resuming activity. This study investigated the possible use of SARS-CoV-2 RNA wastewater surveillance from airline and cruise ship sanitation systems and its potential use as a COVID-19 public health management tool.
Methods
Airline and cruise ship wastewater samples (n = 21) were tested for SARS-CoV-2 RNA using two virus concentration methods, adsorption-extraction by electronegative membrane (n = 13) and ultrafiltration by Amicon (n = 8), and five assays using reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR) and RT-droplet digital PCR (RT-ddPCR). Representative amplicons from positive samples were sequenced to confirm assay specificity.
Results
SARS-CoV-2 RNA was detected in samples from both aircraft and cruise ship wastewater; however, concentrations were near the assay limit of detection. The analysis of multiple replicate samples and use of multiple RT-qPCR and/or RT-ddPCR assays increased detection sensitivity and minimised false-negative results. Representative amplicons were confirmed for the correct PCR product by sequencing. However, differences in sensitivity were observed among assays and concentration methods.
Conclusions
The study indicates that surveillance of wastewater from large transport vessels with their own sanitation systems has potential as a complementary data source to prioritize clinical testing and contact tracing among disembarking passengers. Importantly, sampling methods and molecular assays must be further optimized to maximize sensitivity. The potential for false negatives by both wastewater testing and clinical swab testing suggests that the two strategies could be employed together to maximize the probability of detecting SARS-CoV-2 infections amongst passengers.
Carotenoid production from three strains of Rhodosporidium toruloides grown on glycerol was studied. A time-dependent metabolomics approach was used to understand its metabolism on glycerol and mechanism for carotenoid production in three strains during different growth phases (1, 4, 7, and 12 days). Strain CBS 5490 was the highest carotenoid producer (28.5 mg/L) and had a unique metabolic profile. In this strain, metabolites belonging to the TCA cycle and amino acids were produced in lower amounts, as compared to the other strains. On the other hand, it produced the highest amounts of carotenoid and fatty acid metabolites. This indicated that the lower production of the TCA cycle and amino acid metabolites promoted energy and metabolic flux toward the carotenoid and fatty acid synthesis metabolic pathways. This study shows that metabolomic profiling is a useful tool to gain insight into the metabolic pathways in the cell and to shed light on the different molecular mechanisms between strains.
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.
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