ReviewFrontiers in public health2026
Pathway-driven assessment of wastewater contamination in drinking water systems: integrating AI with public health risk.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wastewater contamination in drinking water systems arises from failures across interconnected components. Globally, an estimated 2.2 billion people lack safely managed drinking water services, with contamination risks persisting in regulated systems. Despite advances in treatment technologies, contamination events continue due to infrastructure deterioration, hydraulic disturbances, and cross-connections, particularly in rapidly urbanizing and resource-constrained regions. A critical limitation of conventional monitoring is its reliance on periodic sampling and laboratory analysis, which often fails to capture transient contamination events and delays response. To address this challenge, artificial intelligence (AI) enables data-driven surveillance through sensor networks, anomaly detection, and predictive modeling. Machine learning and deep learning approaches can identify multivariate contamination patterns, with several studies reporting R
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.