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.
Highlights
Coronavirus (COVID-19) is a new pandemic viral infection that has been spreading worldwide in the year 2020, and is caused by a newly discovered coronavirus
We have described seven nonlinear functions for modeling the time series of the number of daily deaths caused by the COVID-19 pandemic
We considered the time series of daily deaths notified by COVID-19 between January 21, 2020 until July 31, 2020 in USA
Summary
Coronavirus (COVID-19) is a new pandemic viral infection that has been spreading worldwide in the year 2020, and is caused by a newly discovered coronavirus. The pattern of the COVID-19 death curve is highly variable depending on many factors, restrictive measures, and the capacity of the health services in different countries For this reason, it is not possible to use a single function to model the epidemic in different locations in the world. The purpose of our analysis is to match the best nonlinear functional form for the pattern of the expected number of daily deaths curve for each of the four groups, G1-G4, to identify the timing of the peak and the rate and nature of leveling off in the curve Such an understanding is crucial to understanding how COVID-19 deaths differentially progressed in these countries, bringing different approaches for its management and control.
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