Leveraging environmental microbial indicators in wastewater for data-driven disease diagnostics

Area of Science:

• Environmental Science
• Public Health
• Virology

Background:

• Wastewater-based surveillance (WBS) is a valuable tool for monitoring infectious diseases like SARS-CoV-2.
• Environmental factors may influence viral particle prevalence in wastewater.
• Understanding these influences is crucial for effective public health monitoring.

Purpose of the Study:

• To investigate the relationship between environmental factors and SARS-CoV-2 incidence in wastewater.
• To analyze seasonal and spatial variations in viral prevalence across 28 monitoring sites.
• To explore the impact of water quality parameters on SARS-CoV-2 detection.

Main Methods:

• Collected wastewater samples from 28 sites, considering seasonal and spatial variations.
• Measured physicochemical parameters, heavy metals, and minerals; detected SARS-CoV-2 using RT-qPCR.
• Applied correlation analyses and unsupervised learning (K-means, K-medoid clustering) to identify patterns.

Main Results:

• Cluster analysis revealed significant influence of seasonality and water quality on SARS-CoV-2 positivity rates.
• Four distinct clusters showed varied associations between environmental factors and viral prevalence.
• Clustering patterns differed across sites, highlighting diverse environmental impacts on viral detection.

Conclusions:

• Environmental factors, including water quality and seasonality, critically shape SARS-CoV-2 dynamics in wastewater.
• WBS combined with advanced data analysis provides a robust framework for public health surveillance.
• Insights enhance understanding of disease spread in relation to environmental contexts.