Wastewater surveillance using differentiable Gaussian processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Wastewater-based surveillance tracks disease spread within communities by analyzing biological markers in wastewater. A key component of effective wastewater-based surveillance is the reliable inference of underlying viral signals and their changes for accurate interpretation and dissemination. This paper proposes a Bayesian hierarchical modelling framework to jointly estimate wastewater viral signals and their derivatives, while accounting for common features and limitations of wastewater data. Our framework uses differentiable Gaussian processes to model both a common viral trend and deviations at individual stations. Specifically, the common trend is modelled as an Integrated Wiener Process and station-specific signals are smoothed assuming a Matérn covariance function of order 1.5. We demonstrate the framework’s utility by modelling SARS-CoV-2 concentrations across Canada and London, UK, as well as pepper mild mottle virus-normalized respiratory syncytial virus concentrations in Central California. Our results show that this framework reliably estimates both the signal and its derivative in retrospective and surveillance contexts, and show that inference of the signal’s average rates of change is sensitive to the differentiability of the modelling process.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it