用于预测早期传播中随机暴发与消亡的深度学习框架
Deep learning framework for predicting stochastic take-off and die-out of early spreading
AI总结：
针对暴发初期数据不足、现有模型难以预测传播走向的问题，提出首个预测早期传播随机暴发或消亡的深度学习及预训练-微调框架，在不同网络和传染场景下表现优异，可为公共卫生早期干预提供支持。
摘要：
流行病、错误信息或其他有害传播物的大规模暴发给人类社会带来重大威胁，但关于新出现的暴发会升级为重大疫情还是自然消亡这一根本问题，目前仍未得到充分解答。这一问题颇具挑战性，部分原因在于暴发初期数据不足，同时现有模型聚焦于大规模疫情的平均行为，而非小型传播链的随机特性。本文提出首个系统性框架，用于在干预策略仍可有效实施的早期阶段，预测初始传播事件会扩散为重大暴发还是逐渐消亡。我们利用随机传播模型生成的大量数据，开发了一个可实时预测早期传播结果的深度学习框架。在不同传染率水平的Erdős-Rényi和Barabási-Albert网络上的验证结果表明，我们的方法能在潜在暴发发生前准确预测随机传播事件，在不同网络结构和传染场景下均表现出鲁棒性能。为应对暴发初期数据稀疏的挑战，我们进一步提出了一种预训练-微调框架，该框架利用多样化的模拟数据进行预训练，并通过针对性微调适配特定场景。该预训练-微调框架的性能始终优于基线模型，即使在仅使用有限的场景特定数据训练时，也能取得优异表现。据我们所知，这项工作提出了首个预测随机暴发与消亡的框架。该框架为疫情防备和公共卫生决策提供了宝贵见解，有助于制定更科学的早期干预策略。
英文摘要：
Large-scale outbreaks of epidemics, misinformation, or other harmful contagions pose significant threats to human society, yet the fundamental question of whether an emerging outbreak will escalate into a major epidemic or naturally die out remains largely unaddressed. This problem is challenging, partially due to inadequate data during the early stages of outbreaks and also because established models focus on average behaviors of large epidemics rather than the stochastic nature of small transmission chains. Here, we introduce the first systematic framework for forecasting whether initial transmission events will amplify into major outbreaks or fade into extinction during early stages, when intervention strategies can still be effectively implemented. Using extensive data from stochastic spreading models, we developed a deep learning framework that predicts early-stage spreading outcomes in real-time. Validation across Erdős-Rényi and Barabási-Albert networks with varying infectivity levels shows our method accurately forecasts stochastic spreading events well before potential outbreaks, demonstrating robust performance across different network structures and infectivity scenarios.To address the challenge of sparse data during early outbreak stages, we further propose a pretrain-finetune framework that leverages diverse simulation data for pretraining and adapts to specific scenarios through targeted fine-tuning. The pretrain-finetune framework consistently outperforms baseline models, achieving superior performance even when trained on limited scenario-specific data. To our knowledge, this work presents the first framework for predicting stochastic take-off versus die-out. This framework provides valuable insights for epidemic preparedness and public health decision-making, enabling more informed early intervention strategies.
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。
