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.

Quantitative Trend Analysis of SARS-CoV-2 RNA in Municipal Wastewater Exemplified with Sewershed-Specific COVID-19 Clinical Case Counts (preprint)

We present and demonstrate a quantitative statistical linear trend analysis ( QTA ) approach to analyze and interpret SARS-CoV-2 RNA wastewater surveillance results concurrently with clinical case data. This demonstration is based on the work completed under the Ontario (Canada) Wastewater Surveillance Initiative (WSI) by two laboratories in four large sewersheds within the Toronto Public Health (TPH) jurisdiction. The sewersheds were sampled over a 9-month period and data were uploaded to the Ontario Wastewater Surveillance Data and Visualization Hub ( Ontario Dashboard ) along with clinical case counts, both on a sewershed-specific basis. The data from the last 5-months, representing a range of high and low cases, was used for this demonstration. The QTA was conducted on a sewershed specific approach using the recommendations for public health interpretation and use of wastewater surveillance data by the United States Centers for Disease Control and Prevention (US CDC). The interpretation of the QTA results was based on the integration of both clinical and wastewater virus signals using an integration matrix in an interim draft guide by the Public Health Agency of Canada (PHAC). The key steps in the QTA consisted of (i) the calculation of Pepper Mild Mottle Virus (PMMoV), flow and flow-PMMoV-normalized virus loads; (ii) computation of the linear trends including interval estimation to identify the key inflection points using a segmented linear regression method and (iii) integrated interpretations based on consideration of both the cases and wastewater signals, as well as end user actionability. This approach is considered a complementary tool to commonly used qualitative analyses of SARS-CoV-2 RNA in wastewater and is intended to directly support public health decisions using a systematic quantitative approach.

A Wastewater-Based Epidemic Model for SARS-CoV-2 with Application to Three Canadian Cities (preprint)

1The COVID-19 pandemic has stimulated wastewater-based surveillance, allowing public health to track the epidemic by monitoring the concentration of the genetic fingerprints of SARS-CoV-2 shed in wastewater by infected individuals. Wastewater-based surveillance for COVID-19 is still in its infancy. In particular, the quantitative link between clinical cases observed through traditional surveillance and the signals from viral concentrations in wastewater is still developing and hampers interpretation of the data and actionable public-health decisions. We present a modelling framework that includes both SARS-CoV-2 transmission at the population level and the fate of SARS-CoV-2 RNA particles in the sewage system after faecal shedding by infected persons in the population. Using our mechanistic representation of the combined clinical/wastewater system, we perform exploratory simulations to quantify the effect of surveillance effectiveness, public-health interventions and vaccination on the discordance between clinical and wastewater signals. We also apply our model to surveillance data from three Canadian cities to provide wastewater-informed estimates for the actual prevalence, the effective reproduction number and incidence forecasts. We find that wastewater-based surveillance, paired with this model, can complement clinical surveillance by supporting the estimation of key epidemiological metrics and hence better triangulate the state of an epidemic using this alternative data source.