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Authors: Victor Chang 1 ; Oghara Akpomedaye 2 ; Vitor Jesus 1 ; Qianwen Xu 1 ; Karl Hall 2 and Meghana Ganatra 2

Affiliations: 1 Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, U.K. ; 2 Information Systems and AI Research Group, School of Computing and Digital Technologies, Teesside University, Middlesbrough, U.K.

Keyword(s): COVID-19 Prediction, ARIMA, PROPHET, Health Analytics.

Abstract: This study aims to provide insights into predicting future cases of COVID-19 infection and rates of virus transmission in the UK by critically analyzing and visualizing historical COVID-19 data, so that healthcare providers can prepare ahead of time. In order to achieve this goal, the study invested in the existing studies and selected ARIMA and Fb-Prophet time series models as the methods to predict confirmed and death cases in the following year. In a comparison of both models using values of their evaluation metrics, root-mean-square error, mean absolute error and mean absolute percentage error show that ARIMA performs better than Fb-Prophet. The study also discusses the reasons for the dramatic spike in mortality and the large drop in deaths shown in the results, contributing to the literature on health analytics and COVID-19 by validating the results of related studies.

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Paper citation in several formats:
Chang, V.; Akpomedaye, O.; Jesus, V.; Xu, Q.; Hall, K. and Ganatra, M. (2023). Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study. In Proceedings of the 8th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS; ISBN 978-989-758-644-6; ISSN 2184-5034, SciTePress, pages 85-93. DOI: 10.5220/0011990300003485

@conference{complexis23,
author={Victor Chang. and Oghara Akpomedaye. and Vitor Jesus. and Qianwen Xu. and Karl Hall. and Meghana Ganatra.},
title={Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study},
booktitle={Proceedings of the 8th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS},
year={2023},
pages={85-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011990300003485},
isbn={978-989-758-644-6},
issn={2184-5034},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS
TI - Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study
SN - 978-989-758-644-6
IS - 2184-5034
AU - Chang, V.
AU - Akpomedaye, O.
AU - Jesus, V.
AU - Xu, Q.
AU - Hall, K.
AU - Ganatra, M.
PY - 2023
SP - 85
EP - 93
DO - 10.5220/0011990300003485
PB - SciTePress

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