Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm

Varun Agiwal; Jitendra Kumar; Yau Chun Yip

doi:10.20944/preprints202007.0306.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 13 July 2020 / Approved: 14 July 2020 / Online: 14 July 2020 (11:41:59 CEST)

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.

COVID-19; Linear and non-Linear trend; Spline function; Autoregressive Time series model; Bayesian inference

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Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm

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