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