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Waterborne pathogen mitigation: decoding techno-ecological synergies in multiscale transmission networks

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2025-11-05

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2666-6758

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Teng M, Huo Z-Y, Zhang Z, et al., (2025) Waterborne pathogen mitigation: decoding techno-ecological synergies in multiscale transmission networks. The Innovation, Volume 7, Issue 3, March 2026, Article number 101145

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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46 Information and Computing Sciences, 3207 Medical Microbiology, 32 Biomedical and Clinical Sciences, Machine Learning and Artificial Intelligence, Prevention, Infectious Diseases, 2.2 Factors relating to the physical environment, Infection, 3 Good Health and Well Being, environmental pathogen, real-time detection, ecological resilience, decentralized disinfection, data-driven pathogen control

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Attribution-NonCommercial-NoDerivatives 4.0 International

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This work was supported by the National Key R&D Program of China (grants 2022YFC3205400 and 2022YFC3204703), the National Natural Science Foundation of China (grant 52200079), and the UKRI Horizon Europe Guarantee funding of Marie Skłodowska-Curie Actions Postdoctoral Fellowship (grant EP/X022730/1).

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