ReviewInnovation (Cambridge (Mass.))2026
Waterborne pathogen mitigation: Decoding techno-ecological synergies in multiscale transmission networks.
Review in Innovation (Cambridge (Mass.)), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathogen spread and infection represent paramount global challenges, their intricate transmission pathways fundamentally shaped by human behavior and anthropogenic influences. Here, we elucidate pathogen transmission networks in the environment and identify the increasing risks resulting from mutant viruses and resistant bacteria. We examine the advantages and limitations of techniques for pathogen detection and advocate the development of real-time, high-precision, point-of-need assays capable of detecting microorganisms in waterborne matrices, providing a new conceptual and technological approach to future detection methods. We also highlight the inadequate protection of existing centralized disinfection methods and propose the implementation of decentralized disinfection (i.e., chemical-free and energy-efficient point-of-use disinfection) as a form of multi-barrier protection throughout the different pathways of pathogen transmission. A robust and resilient ecosystem can prevent containment sources and inhibit the bioactivity of residual pathogens, and, when working in synergy with multi-barrier disinfection, can achieve a techno-ecological framework for pathogen mitigation. We further address the fact that data-driven technologies (e.g., artificial neural networks and machine learning methods) provide a route for intelligent detection-guided disinfection and the accurate selection of pathogen indicators that are directly relevant to human health. Finally, we highlight concerns regarding potential high-risk pathogens due to climate change.
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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.