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Mo Liu¹, Devan G Becker¹

¹Wilfrid Laurier University, Canada.

|May 21, 2025

概括

通过使用机器学习来识别有冗余信息的网站,可以优化废水监测. 这种方法允许停止非必要的采样地点,节省资源,同时保持疾病监测能力.

科学领域:

环境微生物学环境微生物学
传染病流行病学传染病流行病学
数据科学是数据科学.

背景情况:

废水采样是跟踪传染病动态的一个有价值的工具.
随着COVID-19大流行,废水采样地点的扩大.
随着疫情的消退,需要优化采样策略并降低运营成本.

研究的目的:

开发和评估一种用于识别可停止的废水采样地点的方法.
评估不同采样地点的信息冗余性.
为持续的传染病监测优化资源配置.

主要方法:

利用机器学习模型,根据来自其他站点的数据,预测一个废水站点的每日突变频率.
量化了预测错误,以评估每个站点的独特信息内容.
将该方法应用于来自瑞士五个地点的废水数据.

主要成果:

确定了含有最少独一无二信息的污水采样地点,预测误差最低.
展示了评估预测错误及其解释的系统方法.
在5个瑞士地点中,有2个可以在最少的信息丢失的情况下停止采样.

结论:

基于机器学习的预测错误分析是优化废水监测网络的有效方法.
这种方法使得数据驱动的决策能够决定是否停止采样地点,从而提高资源效率.
这些发现支持基于废水的流行病学适应可持续的传染病监测.

关键词:

机器学习是机器学习.最优的采样设计设计.基于废水的流行病学.

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