Zhang Peiyi, Dong Tianning, Li Ninghui, Liang Faming
Department of Statistics, Purdue University, West Lafayette, IN, USA.
Department of Computer Science, Purdue University, West Lafayette, IN, USA.
J Appl Stat. 2021 Jul 13;50(11-12):2624-2647. doi: 10.1080/02664763.2021.1953449. eCollection 2023.
This paper proposes a dynamic infectious disease model for COVID-19 daily counts data and estimate the model using the Langevinized EnKF algorithm, which is scalable for large-scale spatio-temporal data, converges to the right filtering distribution, and is thus suitable for performing statistical inference and quantifying uncertainty for the underlying dynamic system. Under the framework of the proposed dynamic infectious disease model, we tested the impact of temperature, precipitation, state emergency order and stay home order on the spread of COVID-19 based on the United States county-wise daily counts data. Our numerical results show that warm and humid weather can significantly slow the spread of COVID-19, and the state emergency and stay home orders also help to slow it. This finding provides guidance and support to future policies or acts for mitigating the community transmission and lowering the mortality rate of COVID-19.
本文针对新冠疫情每日数据提出了一种动态传染病模型，并使用朗之万化的集合卡尔曼滤波（EnKF）算法对该模型进行估计。该算法适用于大规模时空数据，能收敛到正确的滤波分布，因此适用于对基础动态系统进行统计推断和不确定性量化。在所提出的动态传染病模型框架下，我们基于美国各县的每日数据，测试了温度、降水、州紧急命令和居家令对新冠疫情传播的影响。我们的数值结果表明，温暖潮湿的天气能够显著减缓新冠疫情的传播，州紧急命令和居家令也有助于减缓疫情传播。这一发现为未来减轻新冠疫情社区传播和降低死亡率的政策或行动提供了指导和支持。
