传染病预测的离散时间序列区间分布的流行病模型
Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction
摘要：
多元计数时间序列模型是分析和预测传染病传播的重要工具。我们认为地方病流行框架，传染病监测计数的自回归模型类，并取代默认的自回归计数从以前的时间段与更灵活的加权方案的启发离散时间序列的间隔分布。我们采用三种不同的参数配方，每一个额外的未知的加权参数估计通过配置文件的似然方法，并比较它们的一个不受限制的非参数方法。新的方法说明了在波多黎各圣胡安登革热发病率的单变量分析，并在柏林的12个区的病毒性胃肠炎的时空研究。我们评估的预测性能的建议模型和几个参考模型在不同的预测范围。在这两个应用中，地方病流行病模型的性能显着改善所提出的加权方案。
英文摘要：
Multivariate count time series models are an important tool for the analysis and prediction of infectious disease spread. We consider the endemic-epidemic framework, an autoregressive model class for infectious disease surveillance counts, and replace the default autoregression on counts from the previous time period with more flexible weighting schemes inspired by discrete-time serial interval distributions. We employ three different parametric formulations, each with an additional unknown weighting parameter estimated via a profile likelihood approach, and compare them to an unrestricted nonparametric approach. The new methods are illustrated in a univariate analysis of dengue fever incidence in San Juan, Puerto Rico, and a spatio-temporal study of viral gastroenteritis in the twelve districts of Berlin. We assess the predictive performance of the suggested models and several reference models at various forecast horizons. In both applications, the performance of the endemic-epidemic models is considerably improved by the proposed weighting schemes.
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