Kumar Jitendra, Agiwal Varun, Yau Chun Yip
Department of Statistics, Central University of Rajasthan, Ajmer, India.
Department of Community Medicine, Jawaharlal Nehru Medical College, Ajmer, India.
Jpn J Stat Data Sci. 2022;5(1):363-377. doi: 10.1007/s42081-021-00127-x. Epub 2021 Jun 7.
A vast majority of the countries are under economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We consider an autoregressive model with a non-linear time trend component that approximately converts into the linear trend using the spline function. The spline function splits the series of COVID-19 into different piecewise segments between respective knots in the form of various growth stages and fits the linear time trend. First, we obtain the number of knots with their locations in the COVID-19 series to identify the transmission stages of COVID-19 infection. Then, the estimation of the model parameters is obtained under the Bayesian setup for the best-fitted model. The results advocate that the proposed model appropriately determines the location of knots based on different transmission stages and know the current transmission situation of the COVID-19 pandemic in a country.
由于当前的2019冠状病毒病（COVID-19）疫情，绝大多数国家正面临经济和健康危机。本研究使用时间序列分析COVID-19，时间序列是了解感染扩大及其变化行为（尤其是趋势模型）的重要工具。我们考虑一个具有非线性时间趋势成分的自回归模型，该模型使用样条函数近似转换为线性趋势。样条函数将COVID-19序列以不同增长阶段的形式在各个节点之间分割成不同的分段，并拟合线性时间趋势。首先，我们在COVID-19序列中获得节点数量及其位置，以识别COVID-19感染的传播阶段。然后，在贝叶斯设置下对最佳拟合模型进行模型参数估计。结果表明，所提出的模型能够根据不同的传播阶段适当地确定节点位置，并了解一个国家COVID-19大流行的当前传播情况。
