Search

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has shown wastewater (WW) surveillance to be an effective means of tracking the emergence of viral lineages which arrive by many routes of transmission including via transportation hubs. In the Canadian province of Ontario, numerous municipal wastewater treatment plants (WWTPs) participate in WW surveillance of infectious disease targets such as SARS-CoV-2 by qPCR and whole genome sequencing (WGS). The Greater Toronto Airports Authority (GTAA), operator of Toronto Pearson International Airport (Toronto Pearson), has been participating in WW surveillance since January 2022. As a major international airport in Canada and the largest national hub, this airport is an ideal location for tracking globally emerging SARS-CoV-2 variants of concern (VOCs). In this study, WW collected from Toronto Pearson's two terminals and pooled aircraft sewage was processed for WGS using a tiled-amplicon approach targeting the SARS-CoV-2 virus genome. Data generated was analyzed to monitor trends of SARS-CoV-2 lineage frequencies. Initial detections of emerging lineages were compared between Toronto Pearson WW samples, municipal WW samples collected from the surrounding regions, and Ontario clinical data as published by Public Health Ontario. Results enabled the early detection of VOCs and individual mutations emerging in Ontario. On average, the emergence of novel lineages at the airport preceded clinical detections by 1-4 weeks, and up to 16 weeks in one case. This project illustrates the efficacy of WW surveillance at transitory transportation hubs and sets an example that could be applied to other viruses as part of a pandemic preparedness strategy and to provide monitoring on a mass scale., (© 2024. The Author(s).)

Wastewater surveillance of SARS-CoV-2 has emerged as a critical tool for tracking the spread of COVID-19. In addition to estimating the relative case numbers using quantitative PCR, SARS-CoV-2 genomic RNA can be extracted from wastewater and sequenced. There are many existing techniques for using the sequenced RNA to determine the relative abundance of known lineages in a sample. However, it is very challenging to predict novel lineages from wastewater data due to its mixed composition and unreliable genomic coverage. In this work, we present a novel technique based on non-negative matrix factorization which is able to reconstruct lineage definitions by analyzing data from across different samples. We test the method both on synthetic and real wastewater sequencing data. We show that the technique is able to determine major lineages such as Omicron and Delta as well as sub-lineages such as BA.5.2.1. We provide a method for determining emerging lineages in wastewater without the need for genomic data from clinical samples. This could be used for routine monitoring of SARS-CoV-2 as well as other emerging viral pathogens in wastewater. Additionally, it may be used to determine more full-genome sequences for viruses with fewer available genomes., (© 2024. The Author(s).)

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.