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An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil
| 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. |
|---|---|
| Autores principais: | Ospina, Raydonal |
| Outros Autores: | Gondim, João A. M.; Leiva, Víctor; Castro, Cecília |
| Assunto: | ARIMA forecasting Epidemiological forecasting Pandemic analytics Predictive modeling Public health intelligence |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866877213686628352 |
|---|---|
| author | Ospina, Raydonal |
| author2 | Gondim, João A. M. Leiva, Víctor Castro, Cecília |
| author2_role | author author author |
| author_facet | Ospina, Raydonal Gondim, João A. M. Leiva, Víctor Castro, Cecília |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Ospina, Raydonal\"},{\"Person.name\":\"Gondim, João A. M.\"},{\"Person.name\":\"Leiva, Víctor\"},{\"Person.name\":\"Castro, Cecília\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Ospina, Raydonal Gondim, João A. M. Leiva, Víctor Castro, Cecília |
| datacite.date.Accepted.fl_str_mv | 2023-07-12T00:00:00Z |
| datacite.date.available.fl_str_mv | 2023-09-13T14:48:32Z |
| datacite.date.embargoed.fl_str_mv | 2023-09-13T14:48:32Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | ARIMA forecasting Epidemiological forecasting Pandemic analytics Predictive modeling Public health intelligence |
| datacite.titles.title.fl_str_mv | An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Ospina, Raydonal Gondim, João A. M. Leiva, Víctor Castro, Cecília |
| dc.date.Accepted.fl_str_mv | 2023-07-12T00:00:00Z |
| dc.date.available.fl_str_mv | 2023-09-13T14:48:32Z |
| dc.date.embargoed.fl_str_mv | 2023-09-13T14:48:32Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/86366 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Multidisciplinary Digital Publishing Institute (MDPI) |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.rights.rights.copyright.fl_str_mv | openAccess |
| dc.subject.none.fl_str_mv | ARIMA forecasting Epidemiological forecasting Pandemic analytics Predictive modeling Public health intelligence |
| dc.title.fl_str_mv | An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorium.uminho.pt/bitstreams/c71a8be6-ea58-4c37-a498-573c66ef980d/download |
| id | rum_fe3d5ef1bf7d519332ccd4c95159b4e8 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/86366 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/86366 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Ospina, Raydonal Gondim, João A. M. Leiva, Víctor Castro, Cecília |
| publishDate | 2023 |
| publisher.none.fl_str_mv | Multidisciplinary Digital Publishing Institute (MDPI) |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engMultidisciplinary Digital Publishing Institute (MDPI)porThis 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.application/pdfporAn overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in BrazilOspina, RaydonalGondim, João A. M.Leiva, VíctorCastro, CecíliaHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISSNIsPartOf2227-7390DOIIsPartOf10.3390/math111430692023-09-13T14:48:32Z2023-07-122023-07-12T00:00:00ZHandlehttps://hdl.handle.net/1822/86366http://purl.org/coar/access_right/c_abf2open accessARIMA forecastingEpidemiological forecastingPandemic analyticsPredictive modelingPublic health intelligence1183372 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal article2023-07-12http://creativecommons.org/licenses/by/4.0/openAccesshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorium.uminho.pt/bitstreams/c71a8be6-ea58-4c37-a498-573c66ef980d/download |
| 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 |
| status | SINGLETON |
| subject.fl_str_mv | ARIMA forecasting Epidemiological forecasting Pandemic analytics Predictive modeling Public health intelligence |
| title | 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_short | 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 |
| topic | ARIMA forecasting Epidemiological forecasting Pandemic analytics Predictive modeling Public health intelligence |
| topic_facet | ARIMA forecasting Epidemiological forecasting Pandemic analytics Predictive modeling Public health intelligence |
| url | https://hdl.handle.net/1822/86366 |
| visible | 1 |