论文标题
美国Covid-19爆发的建模，状态估计和最佳控制
Modeling, state estimation, and optimal control for the US COVID-19 outbreak
论文作者
论文摘要
新型的冠状病毒SARS-COV-2和由此产生的Covid-19疾病的传播蔓延，并继续在全球造成越来越多的死亡人数。尽管疫苗仍在开发中，但社会疏远，广泛的测试以及确认受感染受试者的隔离仍然是遏制大流行的最有效措施。这些措施具有巨大的社会经济成本。在这项工作中，我们介绍了一个新颖的基于优化的决策框架，用于管理美国的Covid-19爆发。这包括对受影响人群的动态进行建模，估算数据中的模型参数和隐藏状态，以及用于测序社交距离和测试事件的最佳控制策略，以便最小化感染的数量。对我们广泛的计算工作的分析表明，在早期实施时，社会距离和隔离是最有效的，并且对确认的受感染受试者的隔离具有更高的影响。此外，我们发现，在严格的社会疏远和放松这种限制之间交替的“开关”政策可以有效地“扁平”曲线，同时可能最大程度地减少社会和经济成本。
The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an unprecedented spread and continue to cause an increasing number of fatalities worldwide. While vaccines are still under development, social distancing, extensive testing, and quarantining of confirmed infected subjects remain the most effective measures to contain the pandemic. These measures carry a significant socioeconomic cost. In this work, we introduce a novel optimization-based decision-making framework for managing the COVID-19 outbreak in the US. This includes modeling the dynamics of affected populations, estimating the model parameters and hidden states from data, and an optimal control strategy for sequencing social distancing and testing events such that the number of infections is minimized. The analysis of our extensive computational efforts reveals that social distancing and quarantining are most effective when implemented early, with quarantining of confirmed infected subjects having a much higher impact. Further, we find that "on-off" policies alternating between strict social distancing and relaxing such restrictions can be effective at "flattening" the curve while likely minimizing social and economic cost.
