Watching the guards: A data-driven method to trigger warnings in national wastewater surveillance networks
Bibliographic record
Abstract
ABSTRACT Surveillance networks have been established in many countries worldwide to monitor SARS-CoV-2 in sewage and to estimate the communal prevalence of COVID-19 cases. Despite their popularity, gaining a rapid understanding of how infectious diseases spread across the territory covered by a network is difficult because of the many factors involved. To improve the detection of warning signals within the territory, we propose to apply principal component analysis (PCA) to screen time-series data generated from wastewater treatment plants (WWTPs) under surveillance. Our analysis allows us to identify single WWTPs deviating from the normal behavior as well as deviations of a cluster of WWTPs (indicative of an intermunicipal outbreak). Our approach is illustrated through the analysis of the dataset generated by the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). Using 10 principal components, we captured 78.6% of the variance in the original dataset of 51 variables (WWTPs). Our analysis identified exceedance of the Q-statistic threshold as evidence of anomalous performance of a single WWTP, and exceedance of the T2-statistic as a sign of an intermunicipal outbreak. Our approach provides a comprehensive picture of the spread of the COVID-19 pandemic, enabling decision-makers to make informed decisions and better manage future pandemics.
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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 | 0.004 | 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".