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<p>Wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the evolution and spread of global health threats, from pathogens to antimicrobial resistances. Throughout the COVID-19 pandemic, multiple wastewater surveillance programmes have advanced statistical and machine learning methods for detecting pathogens from wastewater sequencing data and correlating measured targets with the represented population to infer meaningful conclusions for public health. Integrating contextual data can account for measurement uncertainties across the WBE workflow that affect the reliability of analyses. However, the broader availability and harmonization of data are major obstacles to method development. Here we review the benefits and limitations of wastewater-related data streams, highlighting the potential of machine learning to leverage these streams for normalization and other WBE applications. We emphasize the relevance of developing global frameworks for integrating WBE with other health surveillance systems and discuss next steps to address current and foreseeable challenges for robust and interpretable machine learning-enhanced WBE.</p>

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Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • Eva Aßmann,
  • Timo Greiner,
  • Hugues Richard,
  • Matthew Wade,
  • Shelesh Agrawal,
  • Fabian Amman,
  • Sindy Böttcher,
  • Susanne Lackner,
  • Markus Landthaler,
  • Serghei Mangul,
  • Viorel Munteanu,
  • Fotis Psomopoulos,
  • Maureen Smith,
  • Maria Trofimova,
  • Alexander Ullrich,
  • Max von Kleist,
  • Emanuel Wyler,
  • Martin Hölzer,
  • Christopher Irrgang

摘要

Wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the evolution and spread of global health threats, from pathogens to antimicrobial resistances. Throughout the COVID-19 pandemic, multiple wastewater surveillance programmes have advanced statistical and machine learning methods for detecting pathogens from wastewater sequencing data and correlating measured targets with the represented population to infer meaningful conclusions for public health. Integrating contextual data can account for measurement uncertainties across the WBE workflow that affect the reliability of analyses. However, the broader availability and harmonization of data are major obstacles to method development. Here we review the benefits and limitations of wastewater-related data streams, highlighting the potential of machine learning to leverage these streams for normalization and other WBE applications. We emphasize the relevance of developing global frameworks for integrating WBE with other health surveillance systems and discuss next steps to address current and foreseeable challenges for robust and interpretable machine learning-enhanced WBE.