Real-time estimation of pathogen transmission dynamics from wastewater
Nature Communications·
- DOI
- 10.1038/s41467-026-75380-3
- PMID
- 42436148
- PMCID
- —
- OpenAlex
- W7168044394
- Study type
- Journal article
- Publisher
- Springer Science and Business Media LLC
- Article type
- journal-article
- Integrity
- current
Why this research matters now
Environmental monitoring programs could utilize this analytical approach to maintain continuous epidemiological tracking in areas where traditional clinical reporting is incomplete or resource-constrained.
Structured evidence summary
Research question
How can real-time epidemiological indicators be accurately derived from environmental sewage samples without relying on extensive data preprocessing or parallel clinical records?
Study design
This work introduces a Bayesian semi-mechanistic computational framework evaluated through retrospective application to longitudinal wastewater datasets spanning multiple respiratory viruses.
Population and setting
Data originated from municipal wastewater treatment facilities located in Switzerland, covering six to fourteen sites across three distinct seasonal intervals between November 2022 and May 2025.
Main findings
The proposed algorithm successfully calculated effective reproduction numbers and epidemic growth rates directly from raw viral concentration and hydraulic flow measurements. Model outputs demonstrated stability against measurement variability and performed reliably for influenza and RSV, despite their significantly lower abundance compared to SARS-CoV-2. Predictive accuracy for two-week horizons remained consistent even when sample collection occurred less frequently.
Public-health relevance
Environmental monitoring programs could utilize this analytical approach to maintain continuous epidemiological tracking in areas where traditional clinical reporting is incomplete or resource-constrained.
Important limitations
The authors acknowledge that prior efforts to monitor less prevalent viruses encountered obstacles related to dilute sewage signals, variable host excretion patterns, and a scarcity of corresponding clinical benchmarking data.
GIDS interpretation
This publication contributes a standardized computational protocol for converting raw environmental viral load measurements into actionable epidemiological indicators, supporting broader integration of sewage-based analytics into public health intelligence workflows.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 6 auditable classifier relationships to diseases, places, topics, and study design.