Time Series Regression Models for COVID-19 Deaths
نویسندگان
چکیده
This article develops nonlinear functional forms for modeling count time series of daily deaths due to the COVID-19 virus. Our models explain mean levels while accounting time-varying variances. A Bayesian approach using Markov chain Monte Carlo (MCMC) is adopted analysis, inference and forecasting under proposed models. Applications are shown death counts from several countries affected by pandemic.
منابع مشابه
Time Series Modeling of Coronavirus (COVID-19) Spread in Iran
Various types of Coronaviruses are enveloped RNA viruses from the Corona-viridae family and part of the Coronavirinae subfamily. This family of viruses affects neurological, gastrointestinal, hepatic, and respiratory systems. Recently, a new memb-er of this family, named Covid-19, is moving around the world. The expansion of Covid-19 carries many risks, and its control requires strict planning ...
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ژورنال
عنوان ژورنال: Journal of data science
سال: 2021
ISSN: ['1680-743X', '1683-8602']
DOI: https://doi.org/10.6339/21-jds991