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

Area of Science:

• Environmental science
• Public health
• Epidemiology

Background:

• Sub-city wastewater monitoring offers localized infectious disease surveillance, complementing city-wide data.
• Equitable population representation in sampling frameworks is challenged by demographic data and zone misalignment.

Purpose of the Study:

• Develop a geospatial tool for probabilistic demographic assignment to sub-city sampling zones.
• Evaluate population subgroup representativeness for COVID-19 wastewater surveillance.
• Demonstrate scenario planning to prioritize vulnerable populations.

Main Methods:

• Utilized a geospatial analysis tool to assign census block demographic data to sub-city sampling zones.
• Monitored SARS-CoV-2 in wastewater (November 2021-September 2022) in Davis, California.
• Evaluated four scenarios reducing sampling zones by 25% and 50%, randomly or prioritizing older adults.

Main Results:

• Sub-city wastewater data correlated strongly with centralized treatment plant data (Spearman's correlation 0.909).
• Prioritizing representation increased coverage of individuals over 65 and Black or African American populations.
• Reduced sampling showed minimal impact on data correlation, especially when prioritizing older adults.

Conclusions:

• Probabilistic demographic assignment aids in adapting sampling locations to prioritize vulnerable groups.
• Sub-city wastewater surveillance can maintain data integrity while optimizing sampling strategies.
• This approach enhances equitable public health surveillance for infectious diseases.