相关实验视频
Updated: Jun 9, 2025

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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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根据人口普查数据,以公平为中心的适应性抽样在下下水道区废水监测中使用人口普查数据.
Amita Muralidharan1, Rachel Olson1, C Winston Bess1
1Department of Civil and Environmental Engineering, University of California Davis Davis California 95616 USA hbischel@ucdavis.edu.
概括
郊区废水监测通过使用地理空间工具来提高传染病监测,以公平地代表多样化的人口. 这种方法确保了准确的公共卫生数据,即使采样减少,优先考虑弱势群体.
科学领域:
- 环境科学环境科学
- 公共卫生 公共卫生
- 流行病学 流行病学
背景情况:
- 郊区废水监测提供局部传染病监测,补充全市数据.
- 在抽样框架中,人口的公平代表性受到人口统计数据和区域错位的挑战.
研究的目的:
- 开发一个地理空间工具,用于对分城采样区进行概率化的人口统计分配.
- 评估COVID-19废水监测中的人口子组代表性.
- 展示场景规划,以优先考虑弱势群体.
主要方法:
- 利用地理空间分析工具将人口普查区的人口统计数据分配给城市下面的采样区.
- 在加利福尼亚州戴维斯的废水中监测SARS-CoV-2 (2021年11月至2022年9月).
- 评估了四种方案,分别将采样区减少25%和50%,随机选择或优先考虑老年人.
主要成果:
- 郊区废水数据与集中处理厂数据有很强的相关性 (斯皮尔曼相关性为0.909).
- 优先考虑代表性增加了65岁以上的个人和黑人或非洲裔美国人的覆盖率.
- 减少采样对数据相关性的影响很小,尤其是在优先考虑老年人时.
结论:
- 可能的人口统计分配有助于调整采样地点,以优先考虑弱势群体.
- 郊区废水监测可以保持数据完整性,同时优化采样策略.
- 这种方法增强了对传染病的公平公共卫生监测.
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