Detection of SARS-CoV-2 RNA Throughout Wastewater Treatment Plants and A Modeling Approach to Understand COVID-19 Infection Dynamics in Winnipeg, Canada
Bibliographic record
Abstract
ABSTRACT Although numerous studies have detected SARS-CoV-2 in wastewater and attempted to find correlations between the concentration of SARS-CoV-2 and the number of cases, no consensus has been reached on sample collection and processing, and data analysis. Moreover, the fate of SARS-CoV-2 in wastewater treatment plants is another issue, specifically regarding the discharge of the virus into environmental settings and the water cycle. The current study monitored SARS-CoV-2 in influent and effluent wastewater samples with three different concentration methods and sludge samples over six months (July to December 2020) to compare different virus concentration methods, assess the fate of SARS-CoV-2 in wastewater treatment plants, and describe the potential relationship between SARS-CoV-2 concentrations in influent and infection dynamics. Skimmed milk flocculation (SMF) resulted in higher recoveries (15.27% ± 3.32%) of an internal positive control, Armored RNA, and higher positivity rate of SARS-CoV-2 in samples compared to ultrafiltration methods employing a prefiltration step to eliminate solids. Our results suggested that SARS-CoV-2 may predominate in solids and therefore, concentration methods focusing on both supernatant and solid fractions may result in better recovery. SARS-CoV-2 was detected in influent and primary sludge samples but not in secondary and final effluent samples, indicating a significant reduction during primary and secondary treatments. SARS-CoV-2 was first detected in influent on September 30 th , 2020. A decay-rate formula was applied to estimate initial concentrations of late-processed samples with SMF. A model based on shedding rate and new cases was applied to estimate SARS-CoV-2 concentrations and the number of active shedders. Inferred sensitivity of observed and modeled concentrations to the fluctuations in new cases and test-positivity rates indicated a potential contribution of newly infected individuals to SARS-CoV-2 loads in wastewater.
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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) | 0.001 | 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".