论文标题
在大流行状况下估计疾病的真正负担：SARS-COV2病例
Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case
论文作者
论文摘要
本文介绍了一种用于研究和分析西班牙急性呼吸综合征2（SARS-COV2）流行病的新模型。这是一个隐藏的马尔可夫模型，其隐藏的层是带有泊松移民Po-Inar（1）的再生过程，以及一种机制，允许估计非平稳计数时间序列中报告不足的报道。该模型的新颖性是，在未观察的过程中对创新的期望是定义的时间依赖性函数，以至于通过易感性转移的动力学系统建模有关流行病的信息的信息被整合到模型中。此外，控制不足的报道强度的参数也随时间而变化，以适应数据中可能的季节性或趋势。最大似然方法用于估计模型的参数。
The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a regeneration process with Poisson immigration, Po-INAR(1), together with a mechanism that allows the estimation of the under-reporting in non-stationary count time series. A novelty of the model is that the expectation of the innovations in the unobserved process is a time-dependent function defined in such a way that information about the spread of an epidemic, as modelled through a Susceptible-Infectious-Removed dynamical system, is incorporated into the model. In addition, the parameter controlling the intensity of the under-reporting is also made to vary with time to adjust to possible seasonality or trend in the data. Maximum likelihood methods are used to estimate the parameters of the model.
