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This paper proposes an AI-augmented wastewater-based epidemiology (WBE) framework to predict enteric disease transmission dynamics in low-income communities, using machine learning and time-series modeling to integrate microbial load data, wastewater signals, environmental covariates, and community-level indicators. The framework aims to improve prediction accuracy, reduce detection latency, and support equitable public health decision-making. The study highlights the potential of this integrated approach for scalable and cost-effective disease surveillance in resource-constrained settings.
AI-augmented wastewater epidemiology can potentially improve the prediction and early detection of enteric disease outbreaks in low-income communities, enabling more timely and targeted public health interventions.
Wastewater-based epidemiology (WBE) has emerged as a powerful population-level surveillance approach for monitoring infectious diseases by detecting biomarkers shed into communal wastewater systems. Its relevance is particularly pronounced in low-income communities, where clinical reporting is often fragmented, delayed, or inaccessible, and where enteric diseases remain a major public health burden. Recent advances in artificial intelligence (AI) offer new opportunities to enhance WBE by enabling scalable data integration, pattern recognition, and predictive modeling across complex environmental and socio-demographic contexts. From a broad public health perspective, integrating WBE with AI-driven analytics can transform passive wastewater measurements into proactive early-warning systems capable of informing targeted interventions, optimizing resource allocation, and strengthening outbreak preparedness. This study narrows the focus to enteric disease transmission dynamics in low-income settings, where infrastructural variability, informal sanitation networks, and climate sensitivity complicate traditional surveillance. We propose an AI-augmented WBE framework that combines microbial load data, temporal wastewater signals, environmental covariates, and community-level indicators to model transmission pathways and forecast outbreak risks. By leveraging machine learning and time-series modeling, the framework aims to improve prediction accuracy, reduce detection latency, and support equitable public health decision-making. The integration of WBE and AI thus represents a scalable, cost-effective strategy for strengthening enteric disease surveillance and resilience in resource-constrained communities.
