论文标题
时空感染数据的链接混合物模型，并应用于COVID流行病
A Link Mixture Model for Spatio-temporal Infection Data, with Applications to the COVID Epidemic
论文作者
论文摘要
感染计数的时空模型通常遵循更广泛的疾病映射文献的主题，但可能需要解决时空感染数据的特定特征，包括相当大的时间波动（具有流行阶段）和空间扩散。低阶自动摄影是最近几项关于感染数据的时空研究的特征，可能在区域感染内部和相邻区域的感染上都存在滞后。许多流行时间序列均显示了相对稳定的感染水平（可能是特有性）的时期，然后是感染水平升高的突然急剧阶段。流行高峰之后，有一段时间的降临率并恢复稳定。因此，人们可能会寻求将自回归方案适应这些明显的波动，并暂时偏离平稳性，但随着速度下降和感染恢复流行水平，恢复了平稳性。我们考虑了一种用于感染计数的混合链接模型，该模型允许对爆炸阶段和静态流行性的适应性。两项案例研究应用程序涉及共同地区数据，一个是自共同流行病开始以来的32个伦敦行政区，另一个侧重于与三角洲变体相关的144个地区的流行阶段。
Spatio-temporal models for infection counts generally follow themes of the broader disease mapping literature, but may need to address specific features of spatio-temporal infection data including considerable time fluctuations (with epidemic phases) and spatial diffusion. Low order autoregression is a feature of several recent spatio-temporal studies of infection data, possibly with lags on both within area infections and on infections in adjacent areas. Many epidemic time series show a period of relatively stable infection levels (possibly characterized as endemicity), followed by a sudden sharp phase of increasing infection levels. After the epidemic peak there is a period of descending rates and return to stability. Hence one may seek to adapt the autoregressive scheme to these pronounced fluctuations, with temporary departures from stationarity, but returning to stationarity as rates descend and infections resume endemic levels. We consider a mixture link model for infection counts that allows adaptivity to both explosive phases and static endemicity. Two case study applications involve COVID area-time data, one for 32 London boroughs since the start of the COVID epidemic, the other focusing on the epidemic phase in 144 area of South East England associated with the Delta variant.
