Successful Application of Wastewater-Based Epidemiology in Prediction and Monitoring of the Second Wave of COVID-19 in India with Fragmented Sewerage Systems- A Case Study of Jaipur (India)
Bibliographic record
Abstract
Abstract The present study tracked the city-wide dynamics of severe acute respiratory syndrome-corona virus 2 (SARS-CoV-2) RNA in the wastewater from nine different wastewater treatment plants (WWTPs) in Jaipur during second wave of COVID-19 out-break in India. A total of 164 samples were collected weekly between February 19 th and June 8 th , 2021. SARS-CoV-2 was detected in 47.2% (52/110) influent samples and 37% (20/54) effluent samples. The increasing percentage of positive influent samples correlated with the city’s increasing active clinical cases during the second wave of COVID-19 in Jaipur. Furthermore, WBE based evidence clearly showed early detection of about 20 days (9/9 samples reported positive on April 20 th , 2021) prior to the maximum cases & maximum deaths reported in the city on May 8 th , 2021. The present study further observed the presence of SARS-CoV-2 RNA in treated effluents at the time window of maximum active cases in the city even after tertiary disinfection treatments of UV & Chlorine. The average genome concentration in the effluents and removal efficacy of six commonly used treatments; Activated Sludge Treatment + Chlorine disinfection (ASP + Cl 2) , Moving Bed Biofilm Reactor (MBBR) with Ultraviolet radiations disinfection (MBBR + UV), MBBR + Chlorine (Cl 2 ), Sequencing Batch Reactor (SBR) and SBR + Cl 2 were compared with removal efficacy of SBR + Cl 2 (81.2%)> MBBR + UV (68.8%) > SBR (57.1%) > ASP (50%) > MBBR + Cl 2 (36.4%). The study observed the trends & prevalence of four genes (E, RdRp, N, and ORF1ab gene) based on two different kits and found that prevalence of N> ORF1ab >RdRp> E gene, suggested that the effective genome concentration should be calculated based on the presence/absence of multiple genes. Hence, it is imperative to say that using a combination of different detection genes (E, N, RdRp & ORF1ab genes) reduce false positives in WBE. Graphical Abstract Highlights Successful application of WBE with prediction of 14-20 days for COVID-19 in Jaipur A comparison of SARS-CoV-2 RNA removal efficacy of 9 WWTPs was investigated SBR showed better performance than MBBR with SARS-CoV-2 RNA removal from wastewater Presence of SARS-CoV-2 in effluents even after UV and Chlorine disinfection Using a combination of different detection genes reduce false positives in WBE
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".