论文标题
零膨胀的流行流行模型，该模型具有在德国的麻疹时间序列的应用
A zero-inflated endemic-epidemic model with an application to measles time series in Germany
论文作者
论文摘要
在建模传染病发生时，经常会遇到具有过多零的计数数据。由于非流动时期以及按年龄组或地区，零通货膨胀程度可能会随着时间而变化。现有的地方性流动建模框架（又名HHH）缺乏适当的治疗方法，用于过度零的监视数据，因为它仅限于泊松和负二项式分布。在本文中，我们提出了一个多变量零膨胀的流行流行模型，具有随机效应以扩展HHH。使用分析衍生物，可以通过（惩罚）最大似然推理共同有效地估算新的零通胀和模型HHH部分的参数。一项模拟研究证实了置信区间的适当收敛和覆盖概率。在2005--2018的16个德国州中，将模型应用于麻疹计数，表明零通货膨胀可改善概率预测。
Count data with excessive zeros are often encountered when modelling infectious disease occurrence. The degree of zero inflation can vary over time due to non-epidemic periods as well as by age group or region. The existing endemic-epidemic modelling framework (aka HHH) lacks a proper treatment for surveillance data with excessive zeros as it is limited to Poisson and negative binomial distributions. In this paper, we propose a multivariate zero-inflated endemic-epidemic model with random effects to extend HHH. Parameters of the new zero-inflation and the HHH part of the model can be estimated jointly and efficiently via (penalized) maximum likelihood inference using analytical derivatives. A simulation study confirms proper convergence and coverage probabilities of confidence intervals. Applying the model to measles counts in the 16 German states, 2005--2018, shows that the added zero-inflation improves probabilistic forecasts.
