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.

本文提出了一种针对 COVID-19 每日计数数据的动态传染病模型，并使用 Langevinized EnKF 算法估计该模型，该算法对于大规模时空数据可扩展，收敛到正确的过滤分布，因此适合执行统计推断和量化潜在动态系统的不确定性。在所提出的动态传染病模型的框架下，我们根据美国各县的每日计数数据，测试了气温、降水、州紧急令和居家令对 COVID-19 传播的影响。我们的数值结果表明，温暖潮湿的天气可以显着减缓 COVID-19 的传播，而州紧急状态和居家令也有助于减缓传播速度。这一发现为未来减轻社区传播和降低 COVID-19 死亡率的政策或行动提供了指导和支持。