arXiv 2508.135482025-08-19cs.LG

CALYPSO：结合社区与医疗传播动态预测和分析MRSA感染模式

CALYPSO: Forecasting and Analyzing MRSA Infection Patterns with Community and Healthcare Transmission Dynamics

Rituparna Datta, Jiaming Cui, Gregory R. Madden, Anil Vullikanti

AI总结：

CALYPSO提出一种融合神经网络与机制性集合种群模型的混合框架，利用保险索赔、通勤和医疗转诊数据学习MRSA传播参数，实现多尺度可解释预测，并较机器学习基线提升全州预测性能超过4.5%。

摘要：

耐甲氧西林金黄色葡萄球菌（MRSA）是医院和长期护理机构中一个关键的公共卫生威胁。更好地理解MRSA风险、评估干预措施以及预测MRSA感染率是重要的公共卫生问题。现有预测模型依赖统计或神经网络方法，缺乏流行病学可解释性，且性能有限。机制性流行病模型难以校准，并且在整合多样化数据集方面能力受限。我们提出CALYPSO，这是一个将神经网络与机制性集合种群模型相结合的混合框架，用于捕捉传染病（即MRSA）在医疗和社区环境中的传播动态。我们的模型利用患者级保险索赔数据、通勤数据和医疗转诊模式，学习控制MRSA传播的区域和时间特定参数。这使得能够在多个空间分辨率（县、医疗机构、区域、州）上进行准确且可解释的预测，并支持对感染控制政策和暴发风险的反事实分析。我们还表明，与机器学习基线相比，CALYPSO将全州范围的预测性能提高了超过4.5%，同时识别出高风险区域和具有成本效益的感染预防资源分配策略。

英文摘要：

Methicillin-resistant Staphylococcus aureus (MRSA) is a critical public health threat within hospitals as well as long-term care facilities. Better understanding of MRSA risks, evaluation of interventions and forecasting MRSA rates are important public health problems. Existing forecasting models rely on statistical or neural network approaches, which lack epidemiological interpretability, and have limited performance. Mechanistic epidemic models are difficult to calibrate and limited in incorporating diverse datasets. We present CALYPSO, a hybrid framework that integrates neural networks with mechanistic metapopulation models to capture the spread dynamics of infectious diseases (i.e., MRSA) across healthcare and community settings. Our model leverages patient-level insurance claims, commuting data, and healthcare transfer patterns to learn region- and time-specific parameters governing MRSA spread. This enables accurate, interpretable forecasts at multiple spatial resolutions (county, healthcare facility, region, state) and supports counterfactual analyses of infection control policies and outbreak risks. We also show that CALYPSO improves statewide forecasting performance by over 4.5% compared to machine learning baselines, while also identifying high-risk regions and cost-effective strategies for allocating infection prevention resources.

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