Wastewater-Based Surveillance of Respiratory Syncytial Virus Reveals a Temporal Disconnect in Disease Trajectory across an Active International Land Border
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Conventional metrics for tracking infectious diseases, including case and outbreak data and syndromic surveillance, can be resource-intensive, misleading, and comparatively slow with prolonged data collection, analysis and authentication. This study examined the 2022–2023 Respiratory Syncytial Virus (RSV) season in a contiguous metropolitan area connected by an active international land border, affording an opportunity for comparison of the respiratory virus season spanning two independent public health jurisdictions. Time-lagged cross correlation and qualitative examination of the wastewater signals showed that the peak of the Detroit (MI, USA) RSV season predated the peak in Windsor (ON, Canada) by approximately 5 weeks. A strong positive relationship was observed between RSV N-gene concentrations in wastewater and hospitalization rates in Windsor-Essex (Kendall’s τ = 0.539, p ≤ 0.001, Spearman’s ρ = 0.713, p ≤ 0.001) as well as Detroit (Kendall’s τ = 0.739, p ≤ 0.001, Spearman’s ρ = 0.888, p ≤ 0.001). This study demonstrated that wastewater surveillance can reveal regional differences in infection dynamics between communities and can provide an independent measure of the prevalence of RSV, an underreported disease. These findings support the use of wastewater surveillance as a cost-effective tool in monitoring of RSV to enhance existing surveillance systems and to better inform public health disease mitigation strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".