论文标题
在随机的SIR模型中使用covid-19数据应用的统计推断的过滤方法
A filtering approach for statistical inference in a stochastic SIR model with an application to Covid-19 data
论文作者
论文摘要
在本文中，我们考虑了一个离散的随机SIR模型，其中传播率和传染性个体的真实数量是随机且无法观察的。该模型的一个优点是，它允许我们考虑传染性和未发现感染的随机波动。但是，由于必须在部分信息设置中进行统计推断，因此出现了一个困难。我们采用嵌套粒子过滤方法来估计繁殖率和模型参数。作为案例研究，我们将方法应用于奥地利Covid-19-19感染数据。此外，我们讨论预测和模型测试。
In this paper, we consider a discrete-time stochastic SIR model, where the transmission rate and the true number of infectious individuals are random and unobservable. An advantage of this model is that it permits us to account for random fluctuations in infectiousness and for non-detected infections. However, a difficulty arises because statistical inference has to be done in a partial information setting. We adopt a nested particle filtering approach to estimate the reproduction rate and the model parameters. As a case study, we apply our methodology to Austrian Covid-19 infection data. Moreover, we discuss forecasts and model tests.
