AI-Enabled Early Detection of Emerging Health Risks: Integrating Population Data and Biological Signals

Wouter de Waal

AI-Enabled Early Detection of Emerging Health Risks: Integrating Population Data and Biological Signals

Author(s)	Wouter de Waal
Country	Netherlands
Abstract	Emerging health risks may develop through infectious disease transmission, environmental exposure, antimicrobial resistance, changing pathogen characteristics, occupational hazards, or shifts in population vulnerability. Conventional surveillance systems remain essential, but they can be affected by reporting delays, fragmented databases, incomplete geographic coverage, and limited integration of biological and environmental information. Artificial intelligence may strengthen early detection by analysing patterns across clinical reports, laboratory results, population mobility, environmental measurements, digital activity, wearable sensors, and genomic surveillance.This paper examines the potential contribution of AI-enabled multimodal surveillance to the early identification of emerging health risks. A conceptual review is combined with an illustrative simulation in which an early-warning sensitivity index increases from 58 when clinical reports are used alone to 91 after population trends, environmental indicators, biological signals, and genomic surveillance are integrated. These values illustrate a methodological principle and are not clinical surveillance results. The analysis indicates that multimodal systems may detect weak signals earlier, distinguish local anomalies from wider patterns, and support more timely public-health investigation. However, combining multiple data sources can also increase false alerts, surveillance bias, privacy risk, cybersecurity exposure, and dependence on opaque algorithms. The study argues that AI alerts should initiate structured epidemiological review rather than automatically trigger restrictive public-health interventions. Responsible implementation requires representative data, temporal and geographic validation, uncertainty reporting, transparent alert thresholds, human oversight, data minimization, community accountability, and continuous monitoring for unequal performance. AI can improve public-health preparedness when it supplements laboratory confirmation, clinical expertise, field investigation, and established surveillance infrastructure. Its public value should be evaluated through timeliness, sensitivity, specificity, interpretability, equity, actionability, and the proportionality of any response generated from its predictions.
Keywords	artificial intelligence; early warning; public-health surveillance; population data; biological signals; digital epidemiology; health-risk detection; genomic surveillance
Field	Engineering
Published In	Volume 2, Issue 9, September 2026
Published On	2026-09-05

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AI-Enabled Early Detection of Emerging Health Risks: Integrating Population Data and Biological Signals

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