The acute phase of the Covid-19 pandemic has made apparent the need for decision support based upon accurate epidemic modeling. This process is substantially hampered by under-reporting of cases and related data incompleteness issues. In this article, a discrete-time stochastic epidemic modeling framework is developed with discernible features targeted to publicly available data. The models allow for estimating the total number of infections while accounting for the endemic phase of the pandemic. We assess the prediction of the infection rate utilizing mobility information, notably the principal components of the mobility data. We elaborate upon vector analysis of the epidemic dynamics, thus enriching the traditional tools used for decision making. In particular, we show how certain 2-dimensional plots on the phase plane may yield intuitive information regarding the speed and the type of transmission dynamics.

翻译：新冠疫情的急性阶段凸显了基于精确流行病学建模的决策支持需求。然而，病例漏报及相关数据不完整问题严重阻碍了这一进程。本文构建了一个具有针对公开数据清晰特征的离散时间随机流行病建模框架。这些模型允许在考虑疫情地方性阶段的同时估算总感染人数。我们利用流动性信息（尤其是流动性数据的主成分）评估感染率的预测效果。进一步详细阐述了流行病动力学的向量分析，从而丰富了决策制定的传统工具。特别地，我们展示了相平面上的某些二维图如何提供关于传播动力学速度与类型的直观信息。