处理城市层面的废水监测数据:一个国家空间模型,其中包含了后勤功能
1University of Arizona Mel and Enid Zuckerman College of Public Health Tucson, AZ United States.
Water research
|February 12, 2026
概括
状态空间模型从未检测到的数据准确地估计了废水病原体水平,改善了流行病监测. 这些模型优于替代方法,为疾病动态提供了更好的洞察力.
科学领域:
- 环境微生物学环境微生物学
- 流行病学 流行病学
- 统计建模 统计建模
背景情况:
- 废水监测中的左边审查数据限制了精确的疾病发病率跟踪和早期流行病信号检测.
- 替代等传统方法可能导致对病原体度和流行病学参数的偏见估计.
研究的目的:
- 开发和验证状态空间模型,用于处理废水监测中的左控数据.
- 将这些模型的性能与替代方法进行比较.
- 将模型应用于真实世界数据,以监测甲型流感病毒和呼吸道同胞性病毒.
主要方法:
- 实现状态空间模型与集成后勤模型的实现,以解决左边审查的微生物非检测数据.
- 模拟研究评估模型的准确性,并与替代方法进行比较.
- 开发的模型应用于美国真实废水监测流感A病毒 (IAV) 和呼吸道同胞病毒 (RSV) 的数据.
主要成果:
- 状态空间模型从检测率提供了准确的废水度估计,与替代方法相比,显著降低了平均绝对百分比误差 (0.053比0.39).
- 增加采样频率和样本大小提高了估计准确度,频率更为关键.
- 基于废水的有效繁殖数 (Reww) 估计用于IAV和RSV,使用与临床数据一致的状态空间模型,与显示显著低估和高估的替代方法不同.
结论:
- 国家空间模型有效地处理废水监测中的左控数据,增强对流行病动态的理解.
- 与传统的替代方法相比,开发的模型为估计病原体度和繁殖数量提供了更准确和可靠的方法.
- 该研究提供了可访问的工具和脚本,以促进在公共卫生监测中采用这些先进模型.
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