Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model

Eric N. Aidoo; Richard T. Ampofo; Gaston E. Awashie; Simon K. Appiah; Atinuke O. Adebanji

doi:10.1007/s40808-021-01136-1

Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model

Eric N. Aidoo^*, Richard T. Ampofo, Gaston E. Awashie, Simon K. Appiah, Atinuke O. Adebanji

^*Corresponding author for this work

School of Mathematical & Computer Sciences

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Prediction of COVID-19 incidence and transmissibility rates are essential to inform disease control policy and allocation of limited resources (especially to hotspots), and also to prepare towards healthcare facilities demand. This study demonstrates the capabilities of nonlinear smooth transition autoregressive (STAR) model for improved forecasting of COVID-19 incidence in the Africa sub-region were investigated. Data used in the study were daily confirmed new cases of COVID-19 from February 25 to August 31, 2020. The results from the study showed the nonlinear STAR-type model with logistic transition function aptly captured the nonlinear dynamics in the data and provided a better fit for the data than the linear model. The nonlinear STAR-type model further outperformed the linear autoregressive model for predicting both in-sample and out-of-sample incidence.

Original language	English
Pages (from-to)	961-966
Number of pages	6
Journal	Modeling Earth Systems and Environment
Volume	8
Early online date	26 Feb 2021
DOIs	https://doi.org/10.1007/s40808-021-01136-1
Publication status	Published - Mar 2022

Keywords

Africa
COVID-19
Nonlinearity
Regime switching
Smooth transition
STAR model

ASJC Scopus subject areas

General Environmental Science
General Agricultural and Biological Sciences
Statistics, Probability and Uncertainty
Computers in Earth Sciences

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10.1007/s40808-021-01136-1

Cite this

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title = "Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model",

abstract = "Prediction of COVID-19 incidence and transmissibility rates are essential to inform disease control policy and allocation of limited resources (especially to hotspots), and also to prepare towards healthcare facilities demand. This study demonstrates the capabilities of nonlinear smooth transition autoregressive (STAR) model for improved forecasting of COVID-19 incidence in the Africa sub-region were investigated. Data used in the study were daily confirmed new cases of COVID-19 from February 25 to August 31, 2020. The results from the study showed the nonlinear STAR-type model with logistic transition function aptly captured the nonlinear dynamics in the data and provided a better fit for the data than the linear model. The nonlinear STAR-type model further outperformed the linear autoregressive model for predicting both in-sample and out-of-sample incidence.",

keywords = "Africa, COVID-19, Nonlinearity, Regime switching, Smooth transition, STAR model",

author = "Aidoo, {Eric N.} and Ampofo, {Richard T.} and Awashie, {Gaston E.} and Appiah, {Simon K.} and Adebanji, {Atinuke O.}",

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doi = "10.1007/s40808-021-01136-1",

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AU - Ampofo, Richard T.

AU - Awashie, Gaston E.

AU - Appiah, Simon K.

AU - Adebanji, Atinuke O.

PY - 2022/3

Y1 - 2022/3

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AB - Prediction of COVID-19 incidence and transmissibility rates are essential to inform disease control policy and allocation of limited resources (especially to hotspots), and also to prepare towards healthcare facilities demand. This study demonstrates the capabilities of nonlinear smooth transition autoregressive (STAR) model for improved forecasting of COVID-19 incidence in the Africa sub-region were investigated. Data used in the study were daily confirmed new cases of COVID-19 from February 25 to August 31, 2020. The results from the study showed the nonlinear STAR-type model with logistic transition function aptly captured the nonlinear dynamics in the data and provided a better fit for the data than the linear model. The nonlinear STAR-type model further outperformed the linear autoregressive model for predicting both in-sample and out-of-sample incidence.

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KW - COVID-19

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Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model

Abstract

Keywords

ASJC Scopus subject areas

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