Monitoring SARS-CoV-2 variants in wastewater during periods of low clinical case surveillance in Ethiopia
Bibliographic record
Abstract
Wastewater-based genomic surveillance is a cost-effective approach for tracking outbreaks like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A 2023 study in Addis Ababa, Ethiopia, revealed two infection waves in March and August, with the latter undetected by clinical surveillance. This study analyzed the viral spread and evolution in the population during periods of low reported cases. Viral concentration was performed following the Ceres Technology protocol, and RNA was extracted using the QIAamp Viral RNA Mini Kit. Quantitative PCR was performed using the TaqPath COVID-19 Kit. Samples with cycle threshold values ≤32 were used for sequencing. Library preparation and sequencing were performed using the Illumina COVIDSeq protocol, and data analysis was conducted using the Freyja pipeline on Terra.bio. SARS-CoV-2 viral load in wastewater began rising on 6 March 2023, peaking on 16 March 2023, before declining until early May 2023. A resurgence occurred from 3 to 21 August 2023. In March 2023, XBB.1.5 (34%), XBB* (20%), and CH.1.1 (15%) were dominant. By April-May 2023, XBB.1.5 rose to 51% but declined to 14% in June 2023, while XBB* increased to 41%. In August 2023, XBB* (52%) and XBB.1.5 (31%) co-dominated. Key spike protein mutations (G142D, V213G, T478K, S494P, S477N) correlated with higher viral loads. Wastewater surveillance of SARS-CoV-2 reveals seasonal and behavioral transmission patterns. A March peak linked to XBB.1.5 and XBB* saw XBB.1.5 dominance through May 2023, later declining. An August 2023 resurgence with XBB* co-dominance suggests viral evolution and waning immunity. Key spike mutations correlate with higher viral loads, emphasizing wastewater surveillance's predictive value.IMPORTANCEThis study highlights the critical role of wastewater monitoring in detecting and tracking severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks, particularly in regions with limited clinical reporting. Using genomic analysis tools like Freyja enables the detection and monitoring of SARS-CoV-2 variants by untangling mixed signals to track viral evolution and mutations. This unbiased method offers a comprehensive assessment of virus prevalence, including asymptomatic cases, making it a key supplement to clinical surveillance. By addressing gaps and biases in testing, the detection of two distinct viral waves in Addis Ababa, including one missed by patient-based surveillance, underscores the effectiveness of this approach. The shifting dominance of Omicron sub-lineages, such as XBB.1.5 and XBB*, and their spike protein mutations provide essential insights into viral evolution and transmission dynamics. The connection between specific mutations and increased viral loads further suggests potential impacts on viral fitness and transmissibility. These results reinforce the need to integrate wastewater surveillance into public health strategies to support clinical surveillance, enable early detection of emerging variants, and support timely interventions. Moreover, wastewater surveillance can be extended to monitor other pathogens and antimicrobial resistance, making it an essential tool for pandemic preparedness and ongoing public health management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".