论文标题
病毒传播的分析：随机流行病学模型的过渡模型表示
Analysis of Virus Propagation: A Transition Model Representation of Stochastic Epidemiological Models
论文作者
论文摘要
关于COVID-19的传播的越来越多的文献依赖于各种动态SIR型模型（易感感染的反射），这些模型产生了依赖模型的结果。为了透明度和易于比较结果，我们引入了SIR型随机流行病学模型的共同表示。该表示是一个离散的时间转换模型，它使我们能够相对于状态数量（车厢）及其解释对流行病学模型进行分类。此外，过渡模型消除了本文指出的确定性连续时间流行病学模型的几个局限性。我们还表明，所有SIR型模型都有非线性（伪）状态空间表示形式，并且可以从扩展的Kalman滤波器中估算。
The growing literature on the propagation of COVID-19 relies on various dynamic SIR-type models (Susceptible-Infected-Recovered) which yield model-dependent results. For transparency and ease of comparing the results, we introduce a common representation of the SIR-type stochastic epidemiological models. This representation is a discrete time transition model, which allows us to classify the epidemiological models with respect to the number of states (compartments) and their interpretation. Additionally, the transition model eliminates several limitations of the deterministic continuous time epidemiological models which are pointed out in the paper. We also show that all SIR-type models have a nonlinear (pseudo) state space representation and are easily estimable from an extended Kalman filter.
