Spatiotemporal estimation of actively shedding mpox virus, clade IIb, cases using wastewater signals in British Columbia, Canada
Bibliographic record
Abstract
Wastewater surveillance programs collect sewage influent to monitor for pathogens or chemical signatures. Using wastewater surveillance to detect viral genetic material can offer less biased prevalence estimates of viral infections than test or case-based surveillance systems, but existing methods often overlook key factors like spatiotemporal autocorrelation and the duration of viral shedding. Here, we incorporate wastewater signals within a hierarchical Bayesian spatiotemporal model to estimate active mpox cases, defined as individuals who are actively shedding the virus. Our model allows for estimation of active mpox cases within community health service areas, the most precise geographical level of health administration in British Columbia, Canada over time. The inclusion of a time-varying wastewater signal improved the model fit to active cases (WAIC = 3569, Δ 51), when compared to a null model fit without the wastewater signal (WAIC = 3620). The mean absolute error of mpox active cases was ∼2 cases (95 %CI, 1-4) per community health service area. Our study demonstrates the use of hierarchical Bayesian spatiotemporal models as essential tools in wastewater-based infectious diseases surveillance, emphasizing the importance of spatial and temporal autocorrelation in understanding the patterns in both wastewater signals and actively shedding mpox cases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".