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The analysis and forecasting COVID-19 cases in the United States using Bayesian structural time series models

The analysis and forecasting COVID-19 cases in the United States using Bayesian structural time... In this paper, the Bayesian structural time series model (BSTS) is used to analyze and predict total confirmed cases who infected COVID-19 in the United States from February 28, 2020 through April 6, 2020 using the collect data from CDC (Center of Disease Control) in the United States. It includes variables of days, total confirmed cases, confirmed cases daily, death cases daily, and fatality rates. The author exploits the flexibility of Local Linear Trend, Seasonality, Contemporaneous covariates of dynamic coefficients in the Bayesian structural time series models. In addition, Causal Impact function in R programming is applied to analyze the model and read report of model. The results of the model show that the total confirmed cases who infected COVID-19 will be still most likely to increase straightly, the total numbers infected COVID-19 would be broken through 600,000 in the United States in near future (in the subsequent months). And then arrive at the peak around mid-May 2020. Also, the model suggests that the probability of variable Recovered cases daily is 0.07. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

The analysis and forecasting COVID-19 cases in the United States using Bayesian structural time series models

Biostatistics & Epidemiology , Volume 6 (1): 15 – Jan 2, 2022

The analysis and forecasting COVID-19 cases in the United States using Bayesian structural time series models

Abstract

In this paper, the Bayesian structural time series model (BSTS) is used to analyze and predict total confirmed cases who infected COVID-19 in the United States from February 28, 2020 through April 6, 2020 using the collect data from CDC (Center of Disease Control) in the United States. It includes variables of days, total confirmed cases, confirmed cases daily, death cases daily, and fatality rates. The author exploits the flexibility of Local Linear Trend, Seasonality, Contemporaneous...
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Publisher
Taylor & Francis
Copyright
© 2021 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2021.1948380
Publisher site
See Article on Publisher Site

Abstract

In this paper, the Bayesian structural time series model (BSTS) is used to analyze and predict total confirmed cases who infected COVID-19 in the United States from February 28, 2020 through April 6, 2020 using the collect data from CDC (Center of Disease Control) in the United States. It includes variables of days, total confirmed cases, confirmed cases daily, death cases daily, and fatality rates. The author exploits the flexibility of Local Linear Trend, Seasonality, Contemporaneous covariates of dynamic coefficients in the Bayesian structural time series models. In addition, Causal Impact function in R programming is applied to analyze the model and read report of model. The results of the model show that the total confirmed cases who infected COVID-19 will be still most likely to increase straightly, the total numbers infected COVID-19 would be broken through 600,000 in the United States in near future (in the subsequent months). And then arrive at the peak around mid-May 2020. Also, the model suggests that the probability of variable Recovered cases daily is 0.07.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Jan 2, 2022

Keywords: BSTS; COVID-19; causal impact; trend seasonality; forecast

References