Muralidharan Amita, Olson Rachel, Bess C Winston, Bischel Heather N
Department of Civil and Environmental Engineering, University of California Davis Davis California 95616 USA
Environ Sci (Camb). 2024 Oct 24;11(1):136-151. doi: 10.1039/d4ew00552j. eCollection 2024 Dec 19.
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.
针对传染病的城区以下（或排水流域以下）废水监测提供了一种数据驱动的策略，可为地方公共卫生应对提供信息，并补充来自城市集中污水处理厂的全市范围数据。在城区以下废水采样框架中制定公平代表不同人群的策略，因人口统计数据与采样区域之间的不一致而变得复杂。我们通过以下方式应对这一挑战：（1）开发一种地理空间分析工具，该工具以概率方式将按种族和年龄汇总的亚组人口统计数据从人口普查街区分配到城区以下采样区域；（2）评估加利福尼亚州戴维斯市基于新冠废水的疾病监测中亚组人群的代表性；（3）展示优先考虑弱势群体的情景规划。我们监测了废水中的新冠病毒，以此作为戴维斯市新冠发病率的代理指标（2021年11月至2022年9月）。全市每日采样以及从16个检修孔进行的每周三次城区以下采样覆盖了几乎整个城市人口。作为人口加权平均值汇总的城区以下废水数据与集中处理厂数据高度相关（斯皮尔曼相关性为0.909）。在调整采样地点以优先考虑弱势群体时，人口统计数据的概率分配可为决策提供信息。我们考虑了四种情景，即从基线减少25%和50%的采样区域数量，这些区域是随机选择的，或者是为了优先覆盖65岁以上人群。当去除一半区域时，优先考虑代表性将65岁以上人群的覆盖率从51.1%提高到67.2%，同时将黑人或非裔美国人的覆盖率从67.5%提高到76.7%。缩小规模对城区以下数据与集中数据之间的相关性影响不大（斯皮尔曼相关性范围为0.875至0.917），在优先考虑65岁以上人群覆盖率时观察到最强的相关性。
