ABSTRACT
The COVID-19 pandemic prompted a global integration of wastewater-based epidemiology (WBE) into public health surveillance. Among early pre-COVID practitioners was Greater Tempe (population ~200,000), Arizona, where high-frequency, high-resolution monitoring of opioids began in 2018, leading to unrestricted online data release. Leveraging an existing, neighborhood-level monitoring network, wastewater from eleven contiguous catchment areas was analyzed by RT-qPCR for the SARS-CoV-2 E gene from April 2020 to March 2021 (n=1,556). Wastewater data identified an infection hotspot in a predominantly Hispanic and Native American community, triggering targeted interventions. During the first SARS-CoV-2 wave (June 2020), spikes in virus levels preceded an increase in clinical cases by 8.5+/-2.1 days, providing an early-warning capability that later transitioned into a lagging indicator (-2.0+/-1.4 days) during the December/January 2020-21 wave of clinical cases. Globally representing the first demonstration of immediate, unrestricted WBE data sharing and featuring long-term, innovative, high-frequency, high-resolution sub-catchment monitoring, this successful case study encourages further applications of WBE to inform public health interventions.
Subject(s)
COVID-19ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged from a zoonotic spill-over event and has led to a global pandemic. The public health response has been predominantly informed by surveillance of symptomatic individuals and contact tracing, with quarantine, and other preventive measures have then been applied to mitigate further spread. Non-traditional methods of surveillance such as genomic epidemiology and wastewater-based epidemiology (WBE) have also been leveraged during this pandemic. Genomic epidemiology uses high-throughput sequencing of SARS-CoV-2 genomes to inform local and international transmission events, as well as the diversity of circulating variants. WBE uses wastewater to analyse community spread, as it is known that SARS-CoV-2 is shed through bodily excretions. Since both symptomatic and asymptomatic individuals contribute to wastewater inputs, we hypothesized that the resultant pooled sample of population-wide excreta can provide a more comprehensive picture of SARS-CoV-2 genomic diversity circulating in a community than clinical testing and sequencing alone. In this study, we analysed 91 wastewater samples from 11 states in the USA, where the majority of samples represent Maricopa County, Arizona (USA). With the objective of assessing the viral diversity at a population scale, we undertook a single-nucleotide variant (SNV) analysis on data from 52 samples with >90% SARS-CoV-2 genome coverage of sequence reads, and compared these SNVs with those detected in genomes sequenced from clinical patients. We identified 7973 SNVs, of which 5680 were "novel" SNVs that had not yet been identified in the global clinical-derived data as of 17th June 2020 (the day after our last wastewater sampling date). However, between 17th of June 2020 and 20th November 2020, almost half of the SNVs have since been detected in clinical-derived data. Using the combination of SNVs present in each sample, we identified the more probable lineages present in that sample and compared them to lineages observed in North America prior to our sampling dates. The wastewater-derived SARS-CoV-2 sequence data indicates there were more lineages circulating across the sampled communities than represented in the clinical-derived data. Principal coordinate analyses identified patterns in population structure based on genetic variation within the sequenced samples, with clear trends associated with increased diversity likely due to a higher number of infected individuals relative to the sampling dates. We demonstrate that genetic correlation analysis combined with SNVs analysis using wastewater sampling can provide a comprehensive snapshot of the SARS-CoV-2 genetic population structure circulating within a community, which might not be observed if relying solely on clinical cases.