用图神经网络预测亚利桑那州谷热病发病率，融合环境与病例数据。
Forecasting Coccidioidomycosis (Valley Fever) in Arizona: A Graph Neural Network Approach
- 构建图结构整合病例与土壤、气候等多源环境数据
- 捕捉疾病传播的滞后效应，提升对时间依赖性的建模能力
- 为公共卫生预警和资源调配提供可解释的决策支持
谷热病（Coccidioidomycosis）是美国西南部地方性传染病的显著公共健康问题。本研究首次提出基于图神经网络（GNN）的谷热病发病率预测模型，集成监测病例数据与环境变量，包括土壤状况、大气参数、农业指标及空气质量。模型通过相关性分析挖掘影响疾病传播的关键变量关系，并引入滞后期效应以刻画疾病进展中的关键延迟，增强对复杂时间依赖性的表达能力。实验结果表明，该模型能有效拟合谷热病趋势，揭示其主要环境驱动因素。研究成果有助于构建早期预警系统，并指导高风险区域的防控资源配置。
原文摘要 · Abstract (English)
Coccidioidomycosis, commonly known as Valley Fever, remains a significant public health concern in endemic regions of the southwestern United States. This study develops the first graph neural network (GNN) model for forecasting Valley Fever incidence in Arizona. The model integrates surveillance case data with environmental predictors using graph structures, including soil conditions, atmospheric variables, agricultural indicators, and air quality metrics. Our approach explores correlation-based relationships among variables influencing disease transmission. The model captures critical delays in disease progression through lagged effects, enhancing its capacity to reflect complex temporal dependencies in disease ecology. Results demonstrate that the GNN architecture effectively models Valley Fever trends and provides insights into key environmental drivers of disease incidence. These findings can inform early warning systems and guide resource allocation for disease prevention efforts in high-risk areas.
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