Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series

被引：0

作者：

Shahela, Fahmida Akter ^{[1
]}

Uddin, Nizam ^{[1
]}

机构：

[1] Univ Cent Florida, Dept Stat & Data Sci, Orlando, FL 32816 USA

来源：

BULLETIN OF THE MALAYSIAN MATHEMATICAL SCIENCES SOCIETY | 2022年 / 45卷 / SUPPL 1期

关键词：

ARIMA; Transfer function model; Time series analysis; Forecasts; COVID-19 cases and deaths; Cross-correlations; White noise; Stationarity; Mean absolute error; Mean absolute percentage error; Root mean square error; AIC; SBC;

D O I：

10.1007/s40840-022-01332-x

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported.

引用

页码：461 / 475

页数：15

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