UC Davis

Monitoring wastewater for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—whether performed at a building, neighborhood, or city level—has emerged as a viable way to track the prevalence of Coronavirus Disease-2019 (COVID-19) in a population. This study assesses health equity implications for wastewater sampling paradigms at a sub-city (or sub-sewershed) scale. Many wastewater-based disease surveillance efforts established during the COVID-19 pandemic relied on convenient points of access to sampling locations within the sewer network and/or voluntary participation within a city or region. Sampling in this way may generate public health data that does not equitably represent diverse populations within the area of interest. In preparation for future pandemics, better strategies are needed to design wastewater sampling frameworks. We help address this knowledge gap by: (1) developing a geospatial analysis tool that probabilistically assigns demographic data for subgroup populations aggregated by race and age (which were cited as major risk factors for severe COVID-19 outcomes) from census blocks to sub-sewershed sampling zones; (2) evaluating the representativeness of subgroup populations and sub-sewershed wastewater data in Davis, California, within the sampling framework employed for COVID-19 disease surveillance; and (3) demonstrating a scenario planning strategy in which adaptive sampling prioritizes vulnerable populations (in this case, populations >65 years old). Extensive sub-sewershed wastewater monitoring data was collected in Davis from November 2021 through September 2022, with wastewater samples collected three times per week from 15 maintenance holes (nodes) and daily from the influent of the city’s centralized wastewater treatment plant (WWTP). The sub-city scale sampling achieved near complete coverage of the population, with spatial resolution that informed public health communication initiatives within the city. Wastewater data aggregated from the sub-city scale as a population-weighted mean correlated strongly with wastewater data collected from the centralized treatment plant (Spearman’s Rank correlation coefficient 0.909). We considered four down-scaling scenarios for a reduction in the number of sampling zones from baseline by 25% and 50%, chosen either randomly or by prioritizing maintenance of coverage of the >65-year-old population. Prioritizing representation of this vulnerable population in zone selection increased coverage of >65-year-olds from 51.1% to 67.2% when removing half of the sampling zones, while simultaneously increasing coverage of Black or African American populations from 67.5% to 76.7%. Downscaling the number of sampling zones had little effect overall on the correlation between the sub-sewershed zone wastewater data and centralized WWTP data (Spearman’s Rank correlations ranged from 0.875 to 0.917), but the strongest correlations were obtained when maintaining sampling zones to maximize coverage of the >65-year-old population. When resource constraints necessitate downscaling the number of sampling sites, the approach demonstrated herein can inform decisions in ways that help preserve spatial representation of vulnerable populations, thereby promoting more inclusive, region-specific, and sustainable wastewater monitoring in the future.