SARS-CoV-2 Protein in Wastewater Mirrors COVID-19 Prevalence
Bibliographic record
Abstract
ABSTRACT The COVID-19 pandemic has given rise to diverse approaches to track infections. The causative agent, SARS-CoV-2, is a fecally-shed RNA virus, and many groups have assayed wastewater for viral RNA fragments by quantitative reverse transcription polymerase chain reaction (qRT-PCR) as a proxy for COVID-19 prevalence in the community. Most groups report low levels of viral RNA that often skirt the method’s theoretical limits of detection and quantitation. Here, we demonstrate the presence of SARS-CoV-2 structural proteins in wastewater using traditional immunoblotting and quantitate them from wastewater solids using an immuno-linked PCR method called Multiplex Paired-antibody Amplified Detection (MPAD). MPAD demonstrated facile detection of SARS-CoV-2 proteins compared with SARS-CoV-2 RNA via qRT-PCR in wastewater. In this longitudinal study, we corrected for stochastic variability inherent to wastewater-based epidemiology using multiple fecal content protein biomarkers. These normalized SARS-CoV-2 protein data correlated well with public health metrics. Our method of assaying SARS-CoV-2 protein from wastewater represents a promising and sensitive epidemiological tool to assess prevalence of fecally-shed pathogens in the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".