The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February 2020 to mid January 2021. Using a hierarchical Bayesian framework, we found that the temporal trends of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in the middle of November. However decline and increase of the temporal trend seems to show different patterns in Spain, Italy and Germany.

新型冠状病毒病（COVID-19）在短时间内迅速在世界范围内传播，并且呈现出异质性。了解 COVID-19 传播的潜在时间和空间动态可以导致制定明智和及时的公共卫生政策。在本文中，我们使用时空随机模型来解释 2020 年 2 月下旬至 2021 年 1 月中旬西班牙、意大利和德国每日新增确诊病例数的时空变化。使用分层贝叶斯框架，我们发现这三个国家的疫情时间趋势在4月初迅速达到顶峰并开始缓慢下降，然后在11月中旬开始上升并达到第二个最大值。