School of Health in Social Sciences, University of Edinburgh, Edinburgh, United Kingdom.
School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom.
PLoS One. 2021 Dec 31;16(12):e0260051. doi: 10.1371/journal.pone.0260051. eCollection 2021.
To model the risk of COVID-19 mortality in British care homes conditional on the community level risk.
A two stage modeling process ("doubly latent") which includes a Besag York Mollie model (BYM) and a Log Gaussian Cox Process. The BYM is adopted so as to estimate the community level risks. These are incorporated in the Log Gaussian Cox Process to estimate the impact of these risks on that in care homes.
For an increase in the risk at the community level, the number of COVID-19 related deaths in the associated care home would be increased by exp (0.833), 2. This is based on a simulated dataset. In the context of COVID-19 related deaths, this study has illustrated the estimation of the risk to care homes in the presence of background community risk. This approach will be useful in facilitating the identification of the most vulnerable care homes and in predicting risk to new care homes.
The modeling of two latent processes have been shown to be successfully facilitated by the use of the BYM and Log Gaussian Cox Process Models. Community COVID-19 risks impact on that of the care homes embedded in these communities.
根据社区层面的风险，对英国护理院的 COVID-19 死亡率风险进行建模。
采用两阶段建模过程（“双重潜在”），包括 Besag York Mollie 模型（BYM）和对数高斯 Cox 过程。采用 BYM 来估计社区层面的风险。这些风险被纳入对数高斯 Cox 过程，以估计这些风险对护理院的影响。
对于社区层面风险的增加，相关护理院的 COVID-19 相关死亡人数将增加 exp（0.833），2。这是基于模拟数据集。在 COVID-19 相关死亡的背景下，本研究说明了在存在背景社区风险的情况下对护理院风险的估计。这种方法将有助于识别最脆弱的护理院，并预测新护理院的风险。
通过使用 BYM 和对数高斯 Cox 过程模型，成功地促进了两个潜在过程的建模。社区 COVID-19 风险会影响嵌入这些社区的护理院的风险。
