EpiGuard: An Autonomous Infectious-Disease Early Warning Agent Platform
Infectious disease surveillance is hampered by fragmented data sources, uneven evidence availability across regions, and manual processing delays. EpiGuard proposes an autonomous LLM agent that proactively monitors surveillance feeds, dynamically selects forecasting strategies across absent, sparse, and abundant evidence regimes, and accumulates reusable standard operating procedures from successful warnings. The platform promises faster, more reliable early warning for over 50 diseases, but its experimental validation covers only 15 time series and never tests the absent or sparse branches it advertises.
EpiGuard is a conceptually well-structured agentic platform, but its empirical validation falls far short of its architectural ambitions. The core contributions—evidence-adaptive planning and self-improving SOPs—are described in detail but are not experimentally validated outside the abundant-data regime. The forecasting gains on China and U.S. CDC data are real but modest, and they rely on conventional retrospective model selection rather than demonstrated benefits of LLM-based planning. The paper claims applicability to "more than 50 infectious diseases across different regions" yet evaluates only 15 time series from two countries.
The forecasting model pool combined with retrospective validation-based selection is pragmatic and empirically effective. On the two benchmark datasets, EpiGuard achieves the lowest MASE and RMSE among all compared systems. The unified evidence representation and proactive repository update mechanism provide a clean conceptual framework for handling heterogeneous surveillance sources.
The central evidence-adaptive mechanism is empirically hollow: all quantitative experiments run on datasets with abundant historical observations, so the absent and sparse branches—where LLM reasoning replaces numerical forecasting—are never evaluated. The risk-level rules in Equation 9 use fixed percentile thresholds ($x^T \geq p_{90}$ for CRITICAL, etc.) that appear arbitrary and are not validated against epidemiological outbreak declarations.
The self-improvement mechanism, claimed to enable generalization to unseen diseases and regions, is described only architecturally; no experiment transfers SOPs across diseases or regions. Finally, the evaluation scope is tiny: "The China-Infectious Disease dataset comprises five time series ... while the US-Infectious Disease dataset contains ... ten ... diseases."
The reported improvements of 9.68% and 14.04% over the second-best methods are undermined by an unbalanced baseline roster. Comparing EpiGuard against raw DeepSeek-V4 or GPT-4o mini ReAct for numerical forecasting is a strawman: general-purpose LLM agents lack the specialized model pool and tool use that EpiGuard enjoys. A fairer test would ablate the LLM planner itself—fixing the model pool but removing the APDC—to isolate the contribution of agentic reasoning, yet no such ablation is provided. Table 4 correctly notes that no single time-series foundation model dominates, but this observation alone does not establish that an LLM coordinator is necessary.
Reproducibility is severely limited. The paper provides neither open-source code, prompts, nor the SOP schema, and the supplementary URL offers only a web demo. Critical hyperparameters such as the minimum observation threshold $T_{\min}$ and the exact LLM prompts for absent or sparse risk reasoning are omitted. The retrospective model-selection procedure in Equation 8 holds out the last $H$ observations, but while the text states the task is to predict "the subsequent four weeks," it does not report series lengths or validation details. Without access to planning traces or report templates, independent verification of the agentic behavior is impossible.
Infectious disease outbreaks often spread rapidly before signs of widespread transmission become apparent, leaving a big challenge for rapid response and effective intervention. However, current infectious-disease early-warning systems face three major challenges: fragmented epidemiological evidence across diverse surveillance sources, highly uneven data availability across diseases and geographic regions, and delayed warning procedure caused by manual data collection, analysis, and reporting. To address these challenges, we present EpiGuard, an autonomous, continually self-improving agentic platform for timely and reliable early warning of more than 50 infectious diseases across different regions. EpiGuard proactively monitors and captures newly released official surveillance records, using the latest available evidence for risk assessments. It dynamically adjusts its analytical strategy across three evidence availability regimes, enabling reliable risk assessment under diverse data conditions, ranging from the absence of historical observations to sparse and abundant evidence. Moreover, EpiGuard learns from the successful execution process by preserving effective data-acquisition and analytical procedures as reusable standard operating procedures, enabling it to progressively generalize to previously unseen diseases and regions. We use officially released infectious-disease surveillance data from the China CDC and the United States CDC to evaluate EpiGuard's effectiveness. Experimental results demonstrate that EpiGuard outperforms state-of-the-art forecasting systems, improving forecasting performance by 9.68% and 14.04% on the China CDC and U.S. CDC datasets, respectively.
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