论文标题
一个分层时空模型，用于分析Covid-19的相对风险变化：重点是西班牙，意大利和德国
A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany
论文作者
论文摘要
新型的冠状病毒病（Covid-19）在短时间内迅速在世界范围内迅速传播，并且具有异质模式。了解COVID-19的传播中潜在的时间和空间动态可能会导致知情和及时的公共卫生政策。在本文中，我们使用时空的随机模型来解释从2020年2月下旬到9月中旬在西班牙，意大利和德国每日新确认案件的时间和空间变化。使用分层贝叶斯框架。使用等级制度的贝叶斯框架。我们发现，在三个国家中，他们的峰值迅速达到了峰值，并逐渐达到了他们的峰值，并逐渐达到了峰值，并开始了四月的时间。然而，西班牙的下降和时间趋势的增加似乎变得更加尖锐，在德国更平滑。与意大利和德国相比，西班牙Covid-19的相对风险相对风险的空间异质性也更为明显。
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 to mid September 2020. Using a hierarchical Bayesian framework, we found that the temporal trend 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 August. However decline and increase of the temporal trend seems to be sharper in Spain and smoother in Germany. The spatial heterogeneity of the relative risk of COVID-19 in Spain is also more pronounced than Italy and Germany.
