L-BioStat, KU Leuven, Leuven, 3000, Belgium.
L-BioStat, KU Leuven, Leuven, 3000, Belgium; I-BioStat, Hasselt University, Diepenbeek, 3590, Belgium.
Spat Spatiotemporal Epidemiol. 2023 Jun;45:100588. doi: 10.1016/j.sste.2023.100588. Epub 2023 May 10.
To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.
为了监测古巴的 COVID-19 疫情，每天都在每个直辖市收集有关几个流行病学指标的数据。研究这些指标的时空动态及其相似的行为方式，可以帮助我们更好地了解 COVID-19 在古巴的传播情况。因此，可以使用时空模型来分析这些指标。已经对单变量时空模型进行了深入研究，但是当研究兴趣在于研究多个结果之间的关联时，就需要使用允许空间和时间模式之间存在关联的联合模型。我们研究的目的是开发一个多变量时空模型，以研究 2021 年古巴每周 COVID-19 死亡人数与每周输入 COVID-19 病例数之间的关联。为了允许空间模式之间存在相关性，使用了多变量条件自回归先验（MCAR）。通过使用两种方法考虑时间模式之间的相关性；使用多元随机游走先验或多变量条件自回归先验（MCAR）。所有模型均在贝叶斯框架内进行拟合。
