Spatiotemporal estimation of actively shedding mpox virus, clade IIb, cases using wastewater signals in British Columbia, Canada
Bibliographic record
Abstract
• Wastewater surveillance is useful tool for detecting the presence or absence of a pathogen within a population, but wastewater signals direct relationship with the number of cases is complex and often unknown. • We use a Bayesian hierarchical model to estimate the number of actively shedding mpox cases using wastewater signal while adjusting for spatial and temporal autocorrelation, two often overlooked sources of bias in wastewater surveillance studies. • Actively shedding mpox cases were accurately estimated across time for a finer level of geography than the wastewater treatment plant catchment area which allows for understanding regions, populations or times of increased risk of transmission. • Our work uses a simple model that could be repurposed to wastewater surveillance programs for other viral pathogens, geographies or surveillance strategies. 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 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) | 0.000 | 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".