论文标题
大流行控制的最佳锁定
Optimal Lockdown for Pandemic Control
论文作者
论文摘要
作为传染性疾病遏制的常见策略，封锁不可避免地会削弱经济。持续的共同19-19大流行强调了由公共卫生和经济成本引起的权衡。高度希望解决这一权衡的最佳锁定政策。在这里，我们通过最佳的稳定非均匀锁定锁定了大流行控制的数学框架，我们的目标是尽可能少地减少经济活动，同时以规定的速度减少受感染者的数量。该框架使我们能够有效地计算一般流行病模型的最佳稳定锁定策略，包括经典的SIS/SIR/SEIR/SEIR模型和COVID-19的新模型。我们通过分析县间旅行频率的公开可用数据来分析纽约州62县的Covid-19模型来证明该框架的力量。我们发现，基于2020年4月基于流行病的最佳稳定锁定，与内部相比，纽约市以外的经济活动将更加严格地降低经济活动，尽管该流行病在纽约市当时更为普遍。这种违反直觉的结果突出了大流行控制的复杂性，并阐明了未来的锁定政策设计。
As a common strategy of contagious disease containment, lockdowns will inevitably weaken the economy. The ongoing COVID-19 pandemic underscores the trade-off arising from public health and economic cost. An optimal lockdown policy to resolve this trade-off is highly desired. Here we propose a mathematical framework of pandemic control through an optimal stabilizing non-uniform lockdown, where our goal is to reduce the economic activity as little as possible while decreasing the number of infected individuals at a prescribed rate. This framework allows us to efficiently compute the optimal stabilizing lockdown policy for general epidemic spread models, including both the classical SIS/SIR/SEIR models and a new model of COVID-19 transmissions. We demonstrate the power of this framework by analyzing publicly available data of inter-county travel frequencies to analyze a model of COVID-19 spread in the 62 counties of New York State. We find that an optimal stabilizing lockdown based on epidemic status in April 2020 would have reduced economic activity more stringently outside of New York City compared to within it, even though the epidemic was much more prevalent in New York City at that point. Such a counterintuitive result highlights the intricacies of pandemic control and sheds light on future lockdown policy design.
