Wastewater Surveillance of Influenza (A, B) and Respiratory Syncytial Virus (RSV) in Southern Ontario
Bibliographic record
Abstract
Wastewater-based surveillance (WBS) of SARS-CoV-2 has been highly effective at tracking trends of COVID-19 infections across the globe. This success led to the question of the possible applicability of WBS in monitoring other respiratory viruses that are also posing a concern for healthcare systems. Traditional influenza and RSV surveillance consists of monitoring hospital admissions, clinical testing data and outpatient visits. Even when clinical surveillance and hospitalization admissions are used to monitor the spread of these infections, they are often underestimated, with a lag between detection and community spread. In this study, wastewater surveillance of influenza A, influenza B and respiratory syncytial virus (RSV) was investigated, by quantifying the viral RNA of these viruses in wastewater and comparing these trends to clinical metrices. \nThe solid–liquid partitioning behaviors of influenza A, influenza B, RSV and SARS-CoV-2 in wastewater were examined to understand the behavior of the viruses in wastewater. Analyzing whether each virus is present in the liquid or solid fraction of wastewater may affect the data interpretation and inform further method development. The viral RNA in the liquid and solid fractions were separated and enriched by undergoing different centrifugation settings, overnight polyethylene glycol (PEG) precipitation followed by centrifugation, or ultrafiltration using a Centricon Plus-70 device. The influenza A, influenza B and RSV viral RNA concentrations were found to almost exclusively partition in the solids fraction of wastewater, which increased with an increase in centrifugal settings, unlike SARS-CoV-2 that had a more even split in signal between the solids and liquid fractions. The effectiveness of normalization using the endogenous pepper mild mottled virus (PMMoV) was also examined, by comparing the normalized influenza signal with the raw signal. Normalization using the PMMoV biomarker did not increase or hinder the correlation with clinical testing data relative to the raw influenza signal in wastewater. \nWastewater samples were collected once weekly from two wastewater treatment plants in the Region of Waterloo, Canada, from September 15, 2022, to June 21, 2023. Wastewater was treated overnight with polyethylene glycol (PEG), centrifuged (12,000g 1.5 h), then extracted (pellet) with Qiagen RNeasy PowerMicrobiome Kits and quantified using real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR). The RNA concentrations of influenza A, influenza B and RSV in wastewater strongly correlated with the cases reported by episode date and hospitalizations, in the cities of Kitchener and Waterloo (Spearman’s Rank correlation coefficient rho ranging from 0.41-0.85). Wastewater surveillance can be a very effective additional surveillance tool to support public health officials in monitoring the trends of respiratory viruses in communities. Further development of WBS for respiratory virus monitoring will help public health to better prepare for these and other emerging pathogens in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".