Version 1
: Received: 13 July 2020 / Approved: 14 July 2020 / Online: 14 July 2020 (11:41:59 CEST)
How to cite:
Agiwal, V.; Kumar, J.; Yip, Y.C. Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm. Preprints2020, 2020070306. https://doi.org/10.20944/preprints202007.0306.v1
Agiwal, V.; Kumar, J.; Yip, Y.C. Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm. Preprints 2020, 2020070306. https://doi.org/10.20944/preprints202007.0306.v1
Agiwal, V.; Kumar, J.; Yip, Y.C. Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm. Preprints2020, 2020070306. https://doi.org/10.20944/preprints202007.0306.v1
APA Style
Agiwal, V., Kumar, J., & Yip, Y.C. (2020). Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm. Preprints. https://doi.org/10.20944/preprints202007.0306.v1
Chicago/Turabian Style
Agiwal, V., Jitendra Kumar and Yau Chun Yip. 2020 "Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm" Preprints. https://doi.org/10.20944/preprints202007.0306.v1
Abstract
A vast majority of the countries is under the 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, which is an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We have considered an autoregressive model with a non-linear time trend component that approximately converted into the linear trend using the spline function. The spline function split the COVID-19 series into different piecewise segments between respective knots and fitted the linear time trend. First, we obtain the number of knots with its locations in the COVID-19 series and then the estimation of the best-fitted model parameters are determined under Bayesian setup. The results advocate that the proposed model/methodology is a useful procedure to convert the non-linear time trend into a linear pattern of newly coronavirus case for various countries in the pandemic situation of COVID-19.
Keywords
COVID-19; Linear and non-Linear trend; Spline function; Autoregressive Time series model; Bayesian inference
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
