论文标题
通过深度学习和网络科学模型评估城市锁定对控制COVID-19的影响的影响
Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models
论文作者
论文摘要
COVID-19的特殊认知特征，例如长期孵化期和通过无症状病例的感染，对其爆发的遏制造成了巨大挑战。到2020年3月底，中国已成功控制了Covid-19的扩展，费用很高，以锁定大多数主要城市，包括Epicenter，Wuhan。由于2020年2月中旬之前爆发数据的准确性较低，因此基于早期爆发的统计推断就构成了这些研究的主要技术问题。我们应用监督的学习技术来识别和训练NP-NET-SIR模型，该模型在数据质量较差的情况下证明了强劲的。通过训练有素的模型参数，我们分析了种群流量与跨区域感染连接强度之间的联系，基于一组反事实分析，以研究锁定锁定和锁定措施之间的锁定和可替代性的必要性。我们的发现支持存在与锁定相同的遏制后果的非锁定措施的存在，并为设计更灵活的遏制策略提供了有用的指南。
The special epistemic characteristics of the COVID-19, such as the long incubation period and the infection through asymptomatic cases, put severe challenge to the containment of its outbreak. By the end of March 2020, China has successfully controlled the within-spreading of COVID-19 at a high cost of locking down most of its major cities, including the epicenter, Wuhan. Since the low accuracy of outbreak data before the mid of Feb. 2020 forms a major technical concern on those studies based on statistic inference from the early outbreak. We apply the supervised learning techniques to identify and train NP-Net-SIR model which turns out robust under poor data quality condition. By the trained model parameters, we analyze the connection between population flow and the cross-regional infection connection strength, based on which a set of counterfactual analysis is carried out to study the necessity of lock-down and substitutability between lock-down and the other containment measures. Our findings support the existence of non-lock-down-typed measures that can reach the same containment consequence as the lock-down, and provide useful guideline for the design of a more flexible containment strategy.
