GENIE：面向流行病的生成式神经推理
GENIE: Generative Neural Inference for Epidemics
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中文总结 AI 辅助
该研究针对传统流行病预测模型的不足，提出GENIE这一基于机器学习的时空框架，结合两类编码器，通过模拟训练后在住院高峰等指标上性能优于现有模型。
中文摘要 AI 辅助
SARS-CoV-2大流行凸显了传染病对社会持续构成的风险，以及关于未来可能负担的可靠信息的价值。在以精细空间分辨率预测流行病时，传统使用的机制隔室模型难以捕捉高度复杂的精细传播动态，导致预测不准确且过于自信。然而，详细的基于智能体的模型（ABMs）难以校准，且计算成本过高，无法实时使用。摊销的基于模拟的推理有望克服这一困难，它利用机器学习（ML）的能力，使用任意复杂的流行病模型进行近似预测，且接近实时。在这项工作中，我们引入了面向流行病的生成式神经推理（GENIE），这是一种基于时空ML的框架，用于呼吸道病原体负担的高分辨率预测。GENIE被设计为反映暴发的两个关键特征：（i）不同地点之间共享的生物机制，以及（ii）影响传播动态的地点特定特征。这使得模型架构具有两个模块：（i）局部感染编码器——学习表示所有地点共享的疾病动态，以及（ii）局部概况编码器——学习地点特定的表示。使用来自高分辨率时空ABM的模拟，GENIE被训练为生成未来流行病轨迹的近似后验预测分布的样本。与已建立的统计和ML模型相比，GENIE在一系列指标上表现出优越的性能，包括住院高峰的时间和幅度。
英文摘要
The SARS-CoV-2 pandemic highlighted the ongoing risk infectious diseases pose to society and the value of reliable information on the likely future burden. When forecasting an epidemic at fine spatial resolution, traditionally used mechanistic compartmental model struggle to capture highly complex granular transmission dynamics, resulting in inaccurate and overconfident forecasts. However, detailed Agent-Based Models (ABMs), are challenging to calibrate and are too computationally expensive to use in real-time. Amortized simulation-based inference promises to overcome this difficulty by exploiting the power of machine learning (ML) to perform approximate forecasting at near-real-time using arbitrarily complex models of epidemics. In this work we introduce Generative Neural Inference for Epidemics (GENIE), a spatio-temporal ML-based framework for high-resolution forecasting of the burden of respiratory pathogens. GENIE is designed to reflect two key characteristics of outbreaks: (i) shared biological mechanisms across locations and (ii) location-specific characteristics affecting transmission dynamics. This results in the model architecture having two modules: (i) a Local Infection Encoder - which learns to represent disease dynamics shared across all locations and (ii) a Local Profile Encoder - which learns location-specific representations. Using simulations from a high-resolution spatio-temporal ABM, GENIE is trained to generate samples from an approximate posterior predictive distribution of future epidemic trajectories. Benchmarked against established statistical and ML models, GENIE demonstrates superior performance across a range of measures including the timing and magnitude of peak hospitalisations.
