An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/86366 |
Resumo: | This comprehensive overview focuses on the issues presented by the pandemic due to COVID-19, understanding its spread and the wide-ranging effects of government-imposed restric tions. The overview examines the utility of autoregressive integrated moving average (ARIMA) models, which are often overlooked in pandemic forecasting due to perceived limitations in han dling complex and dynamic scenarios. Our work applies ARIMA models to a case study using data from Recife, the capital of Pernambuco, Brazil, collected between March and September 2020. The research provides insights into the implications and adaptability of predictive methods in the context of a global pandemic. The findings highlight the ARIMA models’ strength in generating accurate short-term forecasts, crucial for an immediate response to slow down the disease’s rapid spread. Accurate and timely predictions serve as the basis for evidence-based public health strategies and interventions, greatly assisting in pandemic management. Our model selection involves an automated process optimizing parameters by using autocorrelation and partial autocorrelation plots, as well as various precise measures. The performance of the chosen ARIMA model is confirmed when comparing its forecasts with real data reported after the forecast period. The study successfully forecasts both confirmed and recovered COVID-19 cases across the preventive plan phases in Recife. However, limitations in the model’s performance are observed as forecasts extend into the future. By the end of the study period, the model’s error substantially increased, and it failed to detect the stabilization and deceleration of cases. The research highlights challenges associated with COVID-19 data in Brazil, such as under-reporting and data recording delays. Despite these limitations, the study emphasizes the potential of ARIMA models for short-term pandemic forecasting while emphasizing the need for further research to enhance long-term predictions. |
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An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in BrazilARIMA forecastingEpidemiological forecastingPandemic analyticsPredictive modelingPublic health intelligenceCiências Naturais::MatemáticasSaúde de qualidadeThis comprehensive overview focuses on the issues presented by the pandemic due to COVID-19, understanding its spread and the wide-ranging effects of government-imposed restric tions. The overview examines the utility of autoregressive integrated moving average (ARIMA) models, which are often overlooked in pandemic forecasting due to perceived limitations in han dling complex and dynamic scenarios. Our work applies ARIMA models to a case study using data from Recife, the capital of Pernambuco, Brazil, collected between March and September 2020. The research provides insights into the implications and adaptability of predictive methods in the context of a global pandemic. The findings highlight the ARIMA models’ strength in generating accurate short-term forecasts, crucial for an immediate response to slow down the disease’s rapid spread. Accurate and timely predictions serve as the basis for evidence-based public health strategies and interventions, greatly assisting in pandemic management. Our model selection involves an automated process optimizing parameters by using autocorrelation and partial autocorrelation plots, as well as various precise measures. The performance of the chosen ARIMA model is confirmed when comparing its forecasts with real data reported after the forecast period. The study successfully forecasts both confirmed and recovered COVID-19 cases across the preventive plan phases in Recife. However, limitations in the model’s performance are observed as forecasts extend into the future. By the end of the study period, the model’s error substantially increased, and it failed to detect the stabilization and deceleration of cases. The research highlights challenges associated with COVID-19 data in Brazil, such as under-reporting and data recording delays. Despite these limitations, the study emphasizes the potential of ARIMA models for short-term pandemic forecasting while emphasizing the need for further research to enhance long-term predictions.This research was partially supported by the National Council for Scientific and Technological Development (CNPq) through the grant 303192/2022-4 (R.O.), and Comissão de Aperfeiçoamento de Pessoal do Nível Superior (CAPES), from the Brazilian government; by FONDECYT, grant number 1200525 (V.L.), from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science and Technology, Knowledge, and Innovation; and by Portuguese funds through the CMAT—Research Centre of Mathematics of University of Minho—within projects UIDB/00013/2020 and UIDP/00013/2020 (C.C.).Multidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoOspina, RaydonalGondim, João A. M.Leiva, VíctorCastro, Cecília2023-07-122023-07-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/86366engOspina, R.; Gondim, J.A.M.; Leiva, V.; Castro, C. An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil. Mathematics 2023, 11, 3069. https://doi.org/10.3390/math111430692227-739010.3390/math111430693069https://www.mdpi.com/2227-7390/11/14/3069info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-21T01:26:11Zoai:repositorium.sdum.uminho.pt:1822/86366Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:29:21.569442Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil |
title |
An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil |
spellingShingle |
An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil Ospina, Raydonal ARIMA forecasting Epidemiological forecasting Pandemic analytics Predictive modeling Public health intelligence Ciências Naturais::Matemáticas Saúde de qualidade |
title_short |
An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil |
title_full |
An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil |
title_fullStr |
An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil |
title_full_unstemmed |
An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil |
title_sort |
An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil |
author |
Ospina, Raydonal |
author_facet |
Ospina, Raydonal Gondim, João A. M. Leiva, Víctor Castro, Cecília |
author_role |
author |
author2 |
Gondim, João A. M. Leiva, Víctor Castro, Cecília |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Ospina, Raydonal Gondim, João A. M. Leiva, Víctor Castro, Cecília |
dc.subject.por.fl_str_mv |
ARIMA forecasting Epidemiological forecasting Pandemic analytics Predictive modeling Public health intelligence Ciências Naturais::Matemáticas Saúde de qualidade |
topic |
ARIMA forecasting Epidemiological forecasting Pandemic analytics Predictive modeling Public health intelligence Ciências Naturais::Matemáticas Saúde de qualidade |
description |
This comprehensive overview focuses on the issues presented by the pandemic due to COVID-19, understanding its spread and the wide-ranging effects of government-imposed restric tions. The overview examines the utility of autoregressive integrated moving average (ARIMA) models, which are often overlooked in pandemic forecasting due to perceived limitations in han dling complex and dynamic scenarios. Our work applies ARIMA models to a case study using data from Recife, the capital of Pernambuco, Brazil, collected between March and September 2020. The research provides insights into the implications and adaptability of predictive methods in the context of a global pandemic. The findings highlight the ARIMA models’ strength in generating accurate short-term forecasts, crucial for an immediate response to slow down the disease’s rapid spread. Accurate and timely predictions serve as the basis for evidence-based public health strategies and interventions, greatly assisting in pandemic management. Our model selection involves an automated process optimizing parameters by using autocorrelation and partial autocorrelation plots, as well as various precise measures. The performance of the chosen ARIMA model is confirmed when comparing its forecasts with real data reported after the forecast period. The study successfully forecasts both confirmed and recovered COVID-19 cases across the preventive plan phases in Recife. However, limitations in the model’s performance are observed as forecasts extend into the future. By the end of the study period, the model’s error substantially increased, and it failed to detect the stabilization and deceleration of cases. The research highlights challenges associated with COVID-19 data in Brazil, such as under-reporting and data recording delays. Despite these limitations, the study emphasizes the potential of ARIMA models for short-term pandemic forecasting while emphasizing the need for further research to enhance long-term predictions. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-12 2023-07-12T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/86366 |
url |
https://hdl.handle.net/1822/86366 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ospina, R.; Gondim, J.A.M.; Leiva, V.; Castro, C. An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil. Mathematics 2023, 11, 3069. https://doi.org/10.3390/math11143069 2227-7390 10.3390/math11143069 3069 https://www.mdpi.com/2227-7390/11/14/3069 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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