Long-term study on wastewater SARS-CoV-2 surveillance across United Arab Emirates

Abstract

Wastewater-based epidemiology (WBE) demonstrates an efficient tool to monitor and predict SARS-CoV-2 community distribution. Many countries across the world have adopted the technique, however, most of these studies were conducted for a short duration with a limited sampling size. In this study, long-term reliability and quantification of wastewater SARS-CoV-2 surveillance is reported via analyzing 16,858 samples collected from 453 different locations across the United Arab Emirates (UAE) from May 2020 to June 2022. The collected composite samples were first incubated at 60 °C followed by filtration, concentration, and then RNA extraction using commercially available kits. The extracted RNA was then analyzed by one-step RT-qPCR and RT-ddPCR, and the data was compared to the reported clinical cases. The average positivity rate in the wastewater samples was found to be 60.61 % (8.41-96.77 %), however, the positivity rate obtained from the RT-ddPCR was significantly higher than the RT-qPCR suggesting higher sensitivity of RT-ddPCR. Time-lagged correlation analysis indicated an increase in positive cases in the wastewater samples when the clinical positive cases declined suggesting that wastewater data are highly affected by the unreported asymptomatic, pre-symptomatic and recovering individuals. The weekly SARS-CoV-2 viral count in the wastewater samples are positively correlated with the diagnosed new clinical cases throughout the studied period and the studied locations. Viral count in wastewater peaked approximately one to two weeks prior to the peaks appearing in active clinical cases indicating that wastewater viral concentrations are effective in predicting clinical cases. Overall, this study further confirms the long-term sensitivity and robust approach of WBE to detect trends in SARS-CoV-2 spread and helps contribute to pandemic management.

Keywords: COVID-19; SARS-CoV-2; WBE surveillance; Wastewater; ddPCR.

Graphical abstract

Fig. 1

Geographic map of UAE showing different emirates by their size.

Fig. 2

The number of wastewater samples tested per month (blue bars) and the positive wastewater samples (red bars) detected in different Emirates. The percentage of positive samples for each emirate is also shown. UAQ: Umm Al Quwain.

Fig. 3

Weekly chart showing the number of (a) clinical tests performed (blue line), the number of positive clinical samples per week (orange bars), the clinical positivity rate (stars) and the weekly administered vaccine doses (circles) as reported by NCEMA (https://covid19.ncema.gov.ae/en), and (b) weekly wastewater samples tested (blue line), the number of weekly positive wastewater samples (orange bars) and the positivity rate in wastewater (stars). Major outbreak periods are indicated.

Fig. 4

Cross-correlation between the positivity rate in clinical samples and wastewater samples. Pearson's correlation coefficients are presented as bars during (a) the first, (b) second, (c) third, and (d) fourth waves. The weekly positivity rates in wastewater samples were shifted ±21 days (by 7 days) to calculate the time-lagged correlation coefficients. The dashed lines represent the threshold for significant correlations.

Fig. 5

Quantified weekly viral load in major WWTPs in six Emirates during outbreak and outbreak-free periods (2021−2022). The viral load (copies/mL) was normalized with reference to the flow rate of the WWTP (m³/day), summed by week for each WWTP and log transformed. AUD: Abu Dhabi, SHJ: Sharjah, AJM: Ajman, UAQ: Umm Al Quwain, RAK: Ras Al Khaimah, FUJ: Fujairah.

Fig. 6

Weekly chart showing the variation of the N1 and N2 averaged and flowrate-normalized viral load in wastewater samples collected from different emirates. The weekly number of newly diagnosed cases is shown as a red dashed line. Major infection waves are indicated in gray arrow and zoomed in. RAK: Ras Al Khaimah, UAQ: Umm Al Quwain.

Fig. 7

The cross-correlation between the viral load in wastewater and the clinical data determined during the fourth COVID-19 wave by shifting the models by 7, 14 and 21 days.