Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study

Victor Chang; Oghara Akpomedaye; Vitor Jesus; Qianwen Xu; Karl Hall; Meghana Ganatra

doi:10.5220/0011990300003485

Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study

Victor Chang, Oghara Akpomedaye, Vitor Jesus, Qianwen Xu, Karl Hall, Meghana Ganatra

Research output: Chapter in Book/Published conference output › Conference publication

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.

Original language	English
Title of host publication	Proceedings of the 8th International Conference on Complexity, Future Information Systems and Risk
Publisher	SciTePress
Pages	85-93
Number of pages	9
Volume	1
ISBN (Electronic)	978-989-758-644-6
DOIs	https://doi.org/10.5220/0011990300003485
Publication status	Published - 22 Apr 2023
Event	8th International Conference on Complexity, Future Information Systems and Risk - Prague, Czech Republic Duration: 22 Apr 2023 → 23 Apr 2023 https://complexis.scitevents.org/?y=2023

Publication series

Name	International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS - Proceedings
Volume	2023-April
ISSN (Electronic)	2184-5034

Conference

Conference	8th International Conference on Complexity, Future Information Systems and Risk
Abbreviated title	Complexis 2023
Country/Territory	Czech Republic
City	Prague
Period	22/04/23 → 23/04/23
Internet address	https://complexis.scitevents.org/?y=2023

Bibliographical note

Funding Information:
This project is partly supported by VC Research (VCR0000199).

Publisher Copyright:
Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Keywords

ARIMA
COVID-19 Prediction
Health Analytics
PROPHET

Access to Document

10.5220/0011990300003485Licence: CC BY-NC-ND 4.0

Cite this

Chang, V., Akpomedaye, O., Jesus, V., Xu, Q., Hall, K., & 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 (Vol. 1, pp. 85-93). (International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS - Proceedings; Vol. 2023-April). SciTePress. https://doi.org/10.5220/0011990300003485

Chang, Victor ; Akpomedaye, Oghara ; Jesus, Vitor et al. / Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study. Proceedings of the 8th International Conference on Complexity, Future Information Systems and Risk. Vol. 1 SciTePress, 2023. pp. 85-93 (International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS - Proceedings).

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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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Chang, V, Akpomedaye, O, Jesus, V , Xu, Q, Hall, K & 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. vol. 1, International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS - Proceedings, vol. 2023-April, SciTePress, pp. 85-93, 8th International Conference on Complexity, Future Information Systems and Risk, Prague, Czech Republic, 22/04/23. https://doi.org/10.5220/0011990300003485

Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study. / Chang, Victor; Akpomedaye, Oghara; Jesus, Vitor et al.
Proceedings of the 8th International Conference on Complexity, Future Information Systems and Risk. Vol. 1 SciTePress, 2023. p. 85-93 (International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS - Proceedings; Vol. 2023-April).

Research output: Chapter in Book/Published conference output › Conference publication

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AU - Jesus, Vitor

AU - Xu, Qianwen

AU - Hall, Karl

AU - Ganatra, Meghana

N1 - Funding Information: This project is partly supported by VC Research (VCR0000199). Publisher Copyright: Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

PY - 2023/4/22

Y1 - 2023/4/22

N2 - 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.

AB - 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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ER -

Chang V, Akpomedaye O, Jesus V , Xu Q, Hall K, Ganatra M. 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. Vol. 1. SciTePress. 2023. p. 85-93. (International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS - Proceedings). doi: 10.5220/0011990300003485

Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this