利用传染模型原型预训练流行病时间序列预测器
Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes
AI总结：
CAPE 是首个开源的流行病预测预训练模型，通过预训练学习流行病学语义，提升预测鲁棒性和泛化能力。
摘要：
准确的流行病预测对于应对爆发至关重要，但现有的数据驱动模型往往容易出错。通常只针对单一病原体训练，它们在新爆发期间面临数据稀缺时表现不佳，并在病毒进化或干预导致的分布变化下失效。然而，数十年的监测数据以及各种不同疾病设计的 compartmental 模型提供了未被利用的可转移知识。为了利用历史中的集体经验，我们提出了 CAPE，第一个开源的流行病预测预训练模型。与现有时间序列基础模型忽视流行病学挑战不同，CAPE 将流行病动态建模为潜在的 compartmental 人口状态的混合，称为 compartmental 原型。它直接从大量的模拟数据中建模一个灵活的 compartment 原型词典，使每个爆发可以表示为随时间变化的混合，将观察到的感染连接到潜在的人口状态。为了促进鲁棒泛化，CAPE 在预训练期间采用 next-token-prediction 方式，并采用轻量级的流行病意识正则化，使学习到的原型与流行病学语义对齐。在涵盖 17 种疾病的全面基准上，CAPE 显著优于强大的基线模型，在零样本预测中表现突出。这项工作代表了预训练流行病模型的系统性进展，这些模型既具有可转移性又具有流行病学基础。我们提供代码在：https://github.com/nuuuh/CAPE。
英文摘要：
Accurate epidemic forecasting is crucial for outbreak preparedness, but existing data-driven models are often brittle. Typically trained on a single pathogen, they struggle with data scarcity during new outbreaks and fail under distribution shifts caused by viral evolution or interventions. However, decades of surveillance data and the design of various compartmental models from diverse diseases offer an untapped source of transferable knowledge. To leverage the collective lessons from history, we propose CAPE, the first open-source pre-trained model for epidemic forecasting. Unlike existing time series foundation models that overlook epidemiological challenges, CAPE models epidemic dynamics as mixtures of latent compartmental population states, termed \textit{compartmental prototypes}. It models a flexible dictionary of compartment prototypes directly from a large collection of simulation data, enabling each outbreak to be expressed as a time-varying mixture that links observed infections to latent population states. To promote robust generalization, CAPE adopts the next-token-prediction paradigm during pre-training with lightweight epidemic-aware regularization that aligns the learned prototypes with epidemiological semantics. On a comprehensive benchmark spanning 17 diseases, CAPE significantly outperforms strong baselines with zero-shot forecasting. This work represents a principled step toward pre-trained epidemic models that are both transferable and epidemiologically grounded. We provide our code in: https://github.com/nuuuh/CAPE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
