Monitoring SARS-CoV-2 variants in wastewater during periods of low clinical case surveillance in Ethiopia

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.

Keywords: COVID-19; Freyja analysis; SARS-CoV-2; genomic surveillance; wastewater based epidemiology.

Fig 1

Trend of Omicron lineage prevalence. (a) Wastewater Omicron lineage prevalence. (b) Clinical-based Omicron lineage prevalence.

Fig 2

Trend of SARS-CoV-2 Omicron lineages’ viral loads in the wastewater of Addis Ababa. Viral load of each omicron lineage at each collection date throughout the year.

Fig 3

Trend of cumulative genome copies of SARS-CoV-2 gene targets in wastewater treatment plants. The data represent the average number of SARS-CoV-2 gene copies for the ORF-1ab gene, N gene, and S million genome copies per L-day of wastewater sample obtained in the untreated wastewater samples from the wastewater treatment plants.

Fig 4

The amino acid mutational analysis of the SARS-CoV-2 scatter plot. The dots in the figure represent the amino acid mutation at each collection date.

Fig 5

The amino acid mutational analysis of the SARS-CoV-2 spike protein scatter plot. The dots in the figure represent the amino acid mutation at each collection date.