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Forecasting COVID-19 in european countries using long short-term memory

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datacite.subject.fosCiências Médicas::Ciências da Saúde
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorCarvalho, Kathleen
dc.contributor.authorTeixeira, Rita de Almeida
dc.contributor.authorReis, Luis Paulo
dc.contributor.authorTeixeira, João Paulo
dc.date.accessioned2025-12-04T15:54:08Z
dc.date.available2025-12-04T15:54:08Z
dc.date.issued2026
dc.description.abstractEffective time series forecasts are increasingly important in supporting judgment in various decisions. Various prediction models are available to support these projections based on how each area provides a diverse set of data with variable behavior. Artificial neural networks (ANNs) significantly contribute to medical research since using predictive ideas allows for the study of disease progression in the future, as well as the behavior of other variables. This study implemented the proposed model based on Long Short-Term Memory (LSTM) to forecast COVID-19 daily new cases, deaths, and ICU patients. The methodology uses quantitative and qualitative data from six European countries: Austria, France, Germany, Italy, Portugal, and Spain to predict the last 242 days of the COVID-19 pandemic. The dataset uses the healthcare parameters of the number of daily new cases, deaths, ICU patients, and mitigation procedures, such as the percentage of the population fully vaccinated, the mandatory use of masks, and the lockdown. Two approaches were used to evaluate the model’s performance: the mean absolute error (MAE) and the mean square error (MSE). The results demonstrate that the LSTM model efficiently captures general trends in COVID-19 metrics but shows limitations when predicting data with low values or high variability, such as daily deaths. The model reported the lowest errors for Spain and Portugal, while France and Germany exhibited higher error rates due to differences in data reporting and pandemic dynamics. These findings highlight the importance of contextualizing predictive models based on specific regional characteristics.eng
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI, UIDB/05757/2020 (DOI:https://doi.org/10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI:https://doi.org/10.54499/UIDB/05757/2020) and SusTEC, LA/P/0007/2020 (DOI:https://doi.org/10.54499/LA/P/0007/2020). Also, the researcher, Kathleen Carvalho, is grateful to the Foundation for Science and Technology (FCT, Portugal) support with the Ph.D. scholarship 2023.05134.BD.
dc.identifier.citationCarvalho, Kathleen; Teixeira, Rita de Almeida; Reis, Luis Paulo; Teixeira, João Paulo (2026). Forecasting COVID-19 in european countries using long short-term memory. In 5th International Conference OL2A 2025. Cham: Springer Nature. p. 227–236. ISBN 9783032001368
dc.identifier.doi10.1007/978-3-032-00137-5_16
dc.identifier.isbn9783032001368
dc.identifier.isbn9783032001375
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.urihttp://hdl.handle.net/10198/35173
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofOptimization, Learning Algorithms and Applications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSupporting decision
dc.subjectCOVID-19
dc.subjectANN
dc.subjectLSTM
dc.titleForecasting COVID-19 in european countries using long short-term memoryeng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.conferenceDate2026
oaire.citation.endPage236
oaire.citation.startPage227
oaire.citation.title5th International Conference
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCarvalho
person.familyNameTeixeira
person.givenNameKathleen
person.givenNameJoão Paulo
person.identifier663194
person.identifier.ciencia-idE61F-8971-5FA1
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0002-8623-7943
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id57069567500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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