Genomic Surveillance of a Canadian Airport Wastewater Samples Allows Early Detection of Emerging SARS-CoV-2 Lineages (preprint)

The SARS-CoV-2 pandemic has shown that wastewater (WW) surveillance is an effective means of tracking the emergence of viral lineages in communities, arriving by many routes including via transportation hubs. In Ontario, numerous municipal 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. Data generated was analyzed to monitor trends 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, emergence of novel lineages at the airport ahead of clinical detections was 1–4 weeks, and up to 16 weeks. 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.

Learning novel SARS-CoV-2 lineages from wastewater sequencing data (preprint)

Background: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.Results:In this work, we present a novel technique based on non-negative matrix factorization which is able to extract novel 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.Conclusions: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 derive more sequences for viruses with fewer complete genomes.