Time Series Regression Models for COVID-19 Deaths

Andrade, Marinho G.; Achcar, Jorge A.; Conceição, Katiane S.; Ravishanker, Nalini

doi:10.6339/21-JDS991

Journal of Data Science

Time Series Regression Models for COVID-19 Deaths

Volume 19, Issue 2 (2021): Special issue: Continued Data Science Contributions to COVID-19 Pandemic, pp. 269–292

Marinho G. Andrade Jorge A. Achcar Katiane S. Conceição All authors (4)

https://doi.org/10.6339/21-JDS991

Pub. online: 7 May 2021 Type: Data Science In Action

Received
1 August 2020

Accepted
1 November 2020

Published
7 May 2021

Abstract

This article develops nonlinear functional forms for modeling count time series of daily deaths due to the COVID-19 virus. Our models explain the mean levels of the time series while accounting for the time-varying variances. A Bayesian approach using Markov chain Monte Carlo (MCMC) is adopted for analysis, inference and forecasting of the time series under the proposed models. Applications are shown for time series of death counts from several countries affected by the pandemic.

Supplementary material

Supplementary Material

Supplementary material online include: rational functions and nonlinear rational polynomial model; tables with fitted model parameters and residual analysis; a brief Report for each of the countries that we considered; data and R code needed to reproduce the results.

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Bayesian approach nonlinear model pandemic cycle

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