| Literature DB >> 34307017 |
Rama Pulicharla1, Guneet Kaur1, Satinder K Brar1.
Abstract
Under the current pandemic situation caused by the novel coronavirus SARS-CoV-2, wastewater monitoring has been increasingly investigated as a surveillance tool for community-wide disease prevalence. After a year into the pandemic, this review critically discusses the real progress made in the detection of SARS-CoV-2 using wastewater monitoring. The limitations and the key challenges faced in improving the detection methods are highlighted. As per the literature, the complex nature of the wastewater matrix poses problems in processing the samples and achieving high sensitivity at low loads of viral RNA using the current detection methods. Furthermore, in the absence of a gold standard analytical method for wastewater, the validation of the generated data for use in wastewater-based epidemiological modeling of the disease becomes practically difficult. However, research is advancing in adopting clinical methods to the wastewater by using appropriate processing controls, and recovery methods. Besides, the technological advances made by the industry including the development of PCR kits with improved detection limits, easy-to-use viral RNA concentration methods, ability to detect the coronavirus variants, and artificial intelligence and advanced data modeling for continuous and remote monitoring greatly help to debottleneck some of these problems. Currently, these technologies are limited to healthcare systems, however, their use for wastewater monitoring is expected to provide opportunities for wide-scale applications of wastewater-based epidemiology (WBE). Moreover, the data from wastewater monitoring act as the initial checkpoint for human health even before the appearance of symptoms, hence WBE needs more attention to manage current and future infectious transmissions.Entities:
Keywords: Artificial intelligence; Automated wastewater sampling; Big data analytics; COVID-19; Digital droplet PCR; Wastewater-based epidemiology
Year: 2021 PMID: 34307017 PMCID: PMC8282934 DOI: 10.1016/j.jece.2021.106063
Source DB: PubMed Journal: J Environ Chem Eng ISSN: 2213-2929
Fig. 1Schematic of steps involved in WBE monitoring of COVID-19 for use in community surveillance.
Fig. 2Available published literature related to COVID-19 since the beginning of the pandemic (based on Scopus web).
Summary of detection methods used for quantification of SARS-CoV-2 RNA in wastewater.
| Raw wastewater, Italy | Two phases (PEG dextran method) | Nested RT-PCR assays (ORF1ab, 332 bp fragment of ORF1ab) and one real time qPCR assay (RdRP gene) | > 500 genome units/reaction | |
| Influent, secondary and tertiary treated effluent, Spain | Aluminum hydroxide adsorption-precipitation | TaqMan real-time RT-PCR (RT-qPCR) | 50 genome units/ reaction | |
| Raw wastewater, Paris | Centrifugation | RT-qPCR primers | 103 genome units/L | |
| Raw wastewater, Japan | – | RT-qPCR | 2000 genome units/L | |
| Raw wastewater, Montana, USA. | Filtration and centrifugation | RT-qPCR (N1 and N2) | 10 genome units/ reaction | |
| Influent, secondary and tertiary treated effluent, Japan | Electronegative membrane-vortex and adsorption | RT-qPCR (N_sarbeco, NIID_2019-n, COV_N CDC-N1) | 4.0 × 103 – 8.2 × 104 copies/L (Influent) 1.4 × 102–2.5 × 103 copies/L (Secondary wastewater) | |
| Influent, secondary and tertiary treated effluent, Louisiana, USA | Ultrafiltration and adsorption–elution method using an electronegative membrane | RT-qPCR | 1.7 × 102 - 10 × 103 - copies/L | |
| Influent and tertiary treated effluent, Germany | Centrifugal ultrafiltration | RT-qPCR for M-gene | 200 genome units/ reaction | |
| Influent and effluent wastewater, Australia | Adsorption–extraction with electronegative and Centrifugal ultrafiltration | RT-ddPCR (CDC N1) | 1000–4000 copies/L |
PCR: Polymerase chain reaction; RT-qPCR: Reverse transcription quantitative polymerase chain reaction; L: Liter; PEG: Polyethylene glycol.
Different measurement units for limit of detection due to different methods developed by various studies.
List of companies and their roles in WBE monitoring of COVID-19.
| Biobot Analytics | United States | Data analytics for development of advanced mathematical models to corelate virus concentration in wastewater with number of cases | Massachusetts Institute of Technology, Massachusetts Water Resources Authority (MWRA) |
| Kando | Israel | IoT sensors and AI algorithms for autonomous sampling, data analysis and live streaming via dashboards | Ben Gurion University, Technion – Israel Institute |
| AquaVitas, LLC | United States | Testing and data analytics for virus surveillance and online dashboards | Arizona State University, US Department of Health and Human Science (HHS), Centre for Disease Control and Prevention (CDC) |
| GoAigua | Spain | IoT sensors, AI/mL algorithms for sampling, data integration and analysis | The Institute of Agrochemistry and Food Technology (IATA), Spanish National Research Council (CSIC) |
| Pace Analytical | United States | Services for lab testing and analysis of wastewater samples | – |
| OSP Microcheck | Canada | Services for lab testing and analysis of wastewater samples; selling of qPCR kits to clients | – |
| Eurofins | Luxembourg | Services for lab testing and analysis of wastewater samples | – |
| LuminUltra | Canada | Development of testing equipment for on-site analysis of wastewater samples | Dalhousie University, Halifax Water |
| GT Molecular | United States | Development of testing method including testing of new UK variant | Colorado State University, Metropolitan State University, State of Colorado |
| CEC Analytics | Canada | Development of sampling equipment | – |
| Teledyne Isco | United States | Development of sampling equipment | – |