论文标题
随着部分观察到的聚合数据的时间变化的马尔可夫过程；冠状病毒的申请
Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus
论文作者
论文摘要
冠状病毒传播分析的主要困难是，许多感染的个体没有表现出COVID-19的症状。这意味着缺乏有关受感染个体以及被恢复和免疫个人的总数的信息。在本文中，我们考虑了参数时间变化的冠状病毒传播的马尔可夫过程，并展示了如何估计模型参数，并近似于每日被感染和检测到的个体和总每天死亡计数中未观察到的计数。这种基于模型的方法在法语数据的应用中说明了。
A major difficulty in the analysis of propagation of the coronavirus is that many infected individuals show no symptoms of Covid-19. This implies a lack of information on the total counts of infected individuals and of recovered and immunized individuals. In this paper, we consider parametric time varying Markov processes of Coronavirus propagation and show how to estimate the model parameters and approximate the unobserved counts from daily numbers of infected and detected individuals and total daily death counts. This model-based approach is illustrated in an application to French data.
