Machine learning-enabled wastewater-based surveillance for emerging pathogen detection and monitoring: current applications, challenges, and future prospects
Anchao Song, Panpan Jia, Jing Luo, Xiaoli Pang, Yongqun He, Yi-Wei Tang · 2026
Read Machine learning-enabled wastewater-based surveillance for emerging pathogen detection and monitoring: current applications, challenges, and future prospects by Anchao Song, Panpan Jia, Jing Luo, Xiaoli Pang, Yongqun He, Yi-Wei Tang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13652061.txt.
Description
Anchao Song and five colleagues review how machine learning is being used to turn wastewater testing into an early warning system for infectious disease. Sewage collects biological traces from whole communities, yet the raw signals are messy and hard to interpret. The authors sorted published work by method and by task, from forecasting outbreaks to spotting unusual changes and cleaning up inconsistent data.
Most of the solid evidence still comes from SARS-CoV-2, and the strongest early-warning results came from simpler statistical methods rather than deep learning. Reported accuracy varied widely with local conditions, and many proposed uses remain untested. The review closes with priorities for the field, including shared standards, better data fusion, and honest measures of uncertainty before such systems guide public decisions.
Catalog record
| CREATOR | Anchao Song, Panpan Jia, Jing Luo, Xiaoli Pang, Yongqun He, Yi-Wei Tang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 110 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |