Department of Applied Statistics, Yonsei University, Seoul, South Korea.
Department of Statistics and Data Science, Yonsei University, Seoul, South Korea.
Stat Med. 2023 Dec 30;42(30):5555-5576. doi: 10.1002/sim.9925. Epub 2023 Oct 9.
Understanding the spatio-temporal patterns of the coronavirus disease 2019 (COVID-19) is essential to construct public health interventions. Spatially referenced data can provide richer opportunities to understand the mechanism of the disease spread compared to the more often encountered aggregated count data. We propose a spatio-temporal Dirichlet process mixture model to analyze confirmed cases of COVID-19 in an urban environment. Our method can detect unobserved cluster centers of the epidemics, and estimate the space-time range of the clusters that are useful to construct a warning system. Furthermore, our model can measure the impact of different types of landmarks in the city, which provides an intuitive explanation of disease spreading sources from different time points. To efficiently capture the temporal dynamics of the disease patterns, we employ a sequential approach that uses the posterior distribution of the parameters for the previous time step as the prior information for the current time step. This approach enables us to incorporate time dependence into our model in a computationally efficient manner without complicating the model structure. We also develop a model assessment by comparing the data with theoretical densities, and outline the goodness-of-fit of our fitted model.
了解 2019 年冠状病毒病（COVID-19）的时空模式对于构建公共卫生干预措施至关重要。与更常见的聚合计数数据相比，具有空间参照的数据可以提供更丰富的机会来了解疾病传播的机制。我们提出了一种时空狄利克雷过程混合模型来分析城市环境中 COVID-19 的确诊病例。我们的方法可以检测到未观察到的疫情集群中心，并估计有用的集群的时空范围，以便构建预警系统。此外，我们的模型可以衡量城市中不同类型地标对疾病传播的影响，这从不同时间点为疾病传播源提供了直观的解释。为了有效地捕捉疾病模式的时间动态，我们采用了一种顺序方法，即将上一时间步的参数后验分布用作当前时间步的先验信息。这种方法使我们能够以计算效率的方式将时间依赖性纳入模型，而不会使模型结构复杂化。我们还通过将数据与理论密度进行比较来评估模型，并概述拟合模型的拟合优度。
