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首页> 外文期刊>Computer Methods in Applied Mechanics and Engineering >COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior
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COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

机译：Covid-19对美国的动态：对人类流动和社会行为的深入学习研究

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This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. The model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19. (C) 2021 Elsevier B.V. All rights reserved.
机译：本文介绍了流行病学系统识别的深度学习框架，噪声和稀疏观察量化的量化不确定性。该方法采用深神经网络的集成，通过制定张量的多步骤损耗函数来推断传染病的时间依赖性再现次数，使我们能够有效地校准多个观察到的轨迹上的模型。该方法应用于Covid-19扩散的移动性和基于社会行为的SEIR模型。该模型在谷歌和无屠杀流动性数据上培训，跨越66天的时间，并且能够在15天的时间窗口内将在203个美国县中的Covid-19传播准确的未来预测。有趣的是，评估不同流动性和社会行为参数的重要性的敏感性分析表明，近距离的出勤率，包括工作场所，住宅和零售和娱乐场所，对有效的生殖号码产生最大的影响。该模型使我们能够迅速探测和量化政府干预的影响，例如锁定和重新开放策略。拟议的框架集合在一起，为数据驱动的流行病学模型发现提供了强大的工作流程，在不确定性下发现，可以明智地为政策和决策提供信息的流行演变的概率预测。此手稿附带的所有代码和数据都可以在https://github.com/predictiveintelligencelab/deepcovid19中获得。 （c）2021 elestvier b.v.保留所有权利。

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