Equity-centered adaptive sampling in sub-sewershed wastewater surveillance using census data

Sub-city, or sub-sewershed, wastewater monitoring for infectious diseases offers a data-driven strategy to inform local public health response and complements city-wide data from centralized wastewater treatment plants. Developing strategies for equitable representation of diverse populations in sub-city wastewater sampling frameworks is complicated by misalignment between demographic data and sampling zones. We address this challenge by: (1) developing a geospatial analysis tool that probabilistically assigns demographic data for subgroups aggregated by race and age from census blocks to sub-city sampling zones; (2) evaluating representativeness of subgroup populations for COVID-19 wastewater-based disease surveillance in Davis, California; and (3) demonstrating scenario planning that prioritizes vulnerable populations. We monitored SARS-CoV-2 in wastewater as a proxy for COVID-19 incidence in Davis (November 2021-September 2022). Daily city-wide sampling and thrice-weekly sub-city sampling from 16 maintenance holes covered nearly the entire city population. Sub-city wastewater data, aggregated as a population-weighted mean, correlated strongly with centralized treatment plant data (Spearman's correlation 0.909). Probabilistic assignment of demographic data can inform decisions when adapting sampling locations to prioritize vulnerable groups. We considered four scenarios that reduced the number of sampling zones from baseline by 25% and 50%, chosen randomly or to prioritize coverage of >65-year-old populations. Prioritizing representation increased coverage of >65-year-olds from 51.1% to 67.2% when removing half the zones, while increasing coverage of Black or African American populations from 67.5% to 76.7%. Downscaling had little effect on correlations between sub-city and centralized data (Spearman's correlations ranged from 0.875 to 0.917), with strongest correlations observed when prioritizing coverage of >65-year-old populations.