Wastewater surveillance of SARS-CoV-2 from aircraft to citywide monitoring

Abstract

Wastewater monitoring is highly efficient in SARS-CoV-2 surveillance for tracking virus spread through travel, surpassing traditional airport passenger testing. This study explored the links between SARS-CoV-2 contents and variants from aircraft to city, assessing the impact of detected variants from international travellers versus the local population. A total of 969 variants using next-generation sequencing (NGS) were examined to understand the links between-aircraft, Arlanda airport, wastewater treatment plants (WWTPs), and Stockholm city-and compared these to variants detected in Stockholm hospitals from January to May 2023. SARS-CoV-2 contents in WWTPs reflected local infection rates, requiring analysis from multiple plants for an accurate city-wide infection assessment. Variants initially detected in aircraft arriving from China did not spread widely during the study period. RT-qPCR is adequate for the detection of specific variants in wastewater, including Variants Under Monitoring. However, NGS remains a powerful method for identifying novel variants. Wastewater monitoring was more effective than clinical testing in the early detection of specific variants, with notable delays observed in clinical surveillance. Furthermore, a broad range of variants are detected in wastewater that surpasses clinical tests. This underscores the vital role of wastewater-based epidemiology in managing future outbreaks and enhancing global health security.

Fig. 1. SARS-CoV-2 content presented as N-gene copy number per PMMoV gene copy.

a Aircraft b Måby station which corresponds to the Airport area c Käppala WWTP. (*) No aircraft sample in weeks 3 and 11. Two biological replicates and two technical replicates were analysed for each data point. Data are presented as mean values ± standard deviation (SD).

Fig. 2. SARS-CoV-2 content presented as Total N-gene copy number normalised using the PMMoV factor approach.

a Käppala WWTP, which is presented as mean values ± standard deviation (SD). b Stockholm, which corresponds to the sum of SARS-CoV-2 contents in the three main WWTPs: Käppala. Henriksdal and Bromma. Two biological replicates and two technical replicates were analysed for each data point.

Fig. 3. SARS-CoV-2 lineage groups detected at different catchment areas including the incoming aircraft from China, the airport area, its associated WWTP: Käppala, a composite sample of all Stockholm WWTP and healthcare data.

The data for patients and composite samples are smoothed; since this approach would not work in the aircraft data, it is not done for the other smaller regions either.

Fig. 4. Overlap between SARS-CoV2 lineages detected in different sites.

a Venn diagram showing the overlap between lineages detected in patients and those detected in Stockholm wastewater, considering either a composite sample (mixed in the lab) or pooled sequencing results from each inlet. b Upset plot of the five analysis levels, from aircraft to patients. An Upset plot shows a horizontal bar plot of the total number of lineages found at each site and on the vertical bar plot the number of lineages found at each combination of sites. Combinations are depicted as coloured dots corresponding to the sites on the left. c Bar plots showing the relative abundance of lineages, coloured after their first site of detection within our dataset. Lineages that could not be fully determined are left blank since their origin is uncertain.