Detection of mpox virus in wastewater provides forewarning of clinical cases in Canadian cities
Bibliographic record
Abstract
Wastewater-based surveillance (WBS) has shown to be an effective tool in monitoring the spread of SARS-CoV-2 and has helped guide public health actions. Consequently, WBS has expanded to now include the monitoring of mpox virus (MPXV) to contribute to its mitigation efforts. In this study, we demonstrate a unique sample processing and a molecular diagnostic strategy for MPXV detection that can inform on the epidemiological situation of mpox outbreaks through WBS. We conducted WBS for MPXV in 22 Canadian wastewater treatment plants (WWTPs) for 14 weeks. Three MPXV qPCR assays were assessed in this study for the detection of MPXV which include the G2R assays (G2R_WA and G2R_G) developed by the Centers for Disease Control and Prevention (CDC) in 2010, and an in-house-developed assay that we have termed G2R_NML. The G2R_NML assay was designed using reference genomes from the 2022 MPXV outbreak and provides a larger qPCR amplicon size to facilitate Sanger sequencing. Results show that all three assays have similar limits of detection and are able to detect the presence of MPXV in wastewater. The G2R_NML assay produced a significantly greater number of Sanger sequence-confirmed MPXV results compared to the CDC G2R assays. Detection of MPXV was possible where provincial surveillance indicated overall low caseloads, and in some sites forewarning of up to several weeks was observed. Overall, this study proposes that WBS of MPXV provides additional information to help fill knowledge gaps in clinical case-surveillance and is potentially an essential component to the management of mpox.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".