Sewer surveillance provides a complimentary approach to clinical disease surveillance as it provides unbiased health and disease burden information at the population level. To better understand SARS-CoV-2 distribution throughout Oregon State (OR) at various scales, wastewater was collected and analyzed for SARS-CoV-2 from the influent of wastewater treatment plants and pump station and manhole locations located throughout sewersheds of Newport (22 locations), Corvallis (8), Boardman/Hermiston (7), Ben (16), Redmond (5), and Eugene/Springfield (17) and at metro-scales including 27 other OR cities. Concurrent to the wastewater sampling event, random households throughout the community were tested for COVID-19 using nasal swab assays. All samples were tested for SARS-CoV-2 RNA with the CDC primers (N1 and N2) using RT-ddPCR (wastewater) or RT-qPCR (nasal swabs). Positive samples were sequenced and analyzed for genetic variants. The SARS-CoV-2 RNA prevalence (positivity) and titer in the wastewater samples ranged from 43 (Newport, OR) to 97% (Redmond, OR) and from log 2.06 to log 6.02 gene copies/L, respectively. Prevalence of SARS-CoV-2 RNA (through analysis of nasal swabs) within the community ranged from 0.1% (Bend, OR) to 17% (Hermiston, OR) which showed a significant correlation (r2=0.92) with SARS-CoV-2 RNA titers in wastewaters. Wastewater SARS-CoV-2 RNA titers also correlated well (r2=0.8) with reported Covid-19 cases. Furthermore, presence/absence and titer concentrations in each micro-sewershed (i.e., the area served by each pump station) were significantly correlated with income per capita in at a neighborhood level within those cities indicating disproportional infections of people living in the poorer areas. This is in line with clinical results showing different demographics were impacted differently. Finally, the average SARS-CoV-2 concentrations found at the wastewater treatment plant influents of 27 Oregon cities also significantly correlated to the per capita/median income of the entire city. This study illustrates that spatially intensive micro-sewershed sampling can be used to identify localized “hotspots” within a given community and begin to understand which external forces (e.g. per capita income, age, education level) may increase the risk of COVID-19 in a given community. Additionally, these correlative factors appear to scale from the neighborhood-scale up to the city-scale.
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 OSU COVID-19 Wastewater-based Epidemiology slides | 5.99 MB |