论文标题
通过贝叶斯时变模型分析Covid-19的初始阶段
Analyzing initial stage of COVID-19 transmission through Bayesian time-varying model
论文作者
论文摘要
这部小说《冠状病毒covid-19》最近爆发以一种或另一种方式影响了我们的所有生命。尽管医学研究人员正在努力寻找治疗方法，并在受影响的个人中找到治疗方法，但诸如“锁定”，``在家居住''之类的措施正在世界各地实施``社会距离''，以进一步遏制其进一步的传播。为了建模非平稳差异，我们提出了一种新颖的时变时间变化的半参数AR $（p）$模型，用于每天收集的新受影响病例的计数有价值的时间序列，并将其扩展为提出一种新型的时间变化的Ingarch模型。我们提出的模型结构适合哈密顿蒙特卡洛（HMC）采样，以进行有效的计算。我们通过与某些接近现有方法相比表现出优势的模拟来证实我们的方法。最后，我们分析了新确认的案件的日常时间序列数据，以通过不同的政府干预来研究其传播。
Recent outbreak of the novel coronavirus COVID-19 has affected all of our lives in one way or the other. While medical researchers are working hard to find a cure and doctors/nurses to attend the affected individuals, measures such as `lockdown', `stay-at-home', `social distancing' are being implemented in different parts of the world to curb its further spread. To model the non-stationary spread, we propose a novel time-varying semiparametric AR$(p)$ model for the count valued time-series of newly affected cases, collected every day and also extend it to propose a novel time-varying INGARCH model. Our proposed structures of the models are amenable to Hamiltonian Monte Carlo (HMC) sampling for efficient computation. We substantiate our methods by simulations that show superiority compared to some of the close existing methods. Finally we analyze the daily time series data of newly confirmed cases to study its spread through different government interventions.
