ARIES: A Scalable Multi-Agent Orchestration Framework for Real-Time Epidemiological Surveillance and Outbreak Monitoring

TL;DR

ARIES is a multi-agent framework that enhances real-time epidemiological surveillance by autonomously querying and synthesizing data from multiple sources, outperforming generic AI models in outbreak detection.

Contribution

The paper introduces ARIES, a novel hierarchical multi-agent system tailored for epidemiological data integration and real-time threat detection, advancing beyond static dashboards.

Findings

ARIES autonomously queries WHO, CDC, and research papers.

It synthesizes data to identify emergent health threats.

Outperforms generic models in outbreak detection tasks.

Abstract

Global health surveillance is currently facing a challenge of Knowledge Gaps. While general-purpose AI has proliferated, it remains fundamentally unsuited for the high-stakes epidemiological domain due to chronic hallucinations and an inability to navigate specialized data silos. This paper introduces ARIES (Agentic Retrieval Intelligence for Epidemiological Surveillance), a specialized, autonomous multi-agent framework designed to move beyond static, disease-specific dashboards toward a dynamic intelligence ecosystem. Built on a hierarchical command structure, ARIES utilizes GPTs to orchestrate a scalable swarm of sub-agents capable of autonomously querying World Health Organization (WHO), Center for Disease Control and Prevention (CDC), and peer-reviewed research papers. By automating the extraction and logical synthesis of surveillance data, ARIES provides a specialized reasoning that identifies emergent threats and signal divergence in near real-time. This modular architecture proves that a task-specific agentic swarm can outperform generic models, offering a robust, extensible for next-generation outbreak response and global health intelligence.