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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".