Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/34396
Autoria: Duarte, M.
Ferreira da Silva, C.
Moro, S.
Data: 2025
Título próprio: Machine learning models to predict the COVID-19 reproduction rate: Combining non-pharmaceutical interventions with sociodemographic and cultural characteristics
Título da revista: Informatics for Health and Social Care
Volume: 50
Número: 2
Paginação: 81 - 99
Referência bibliográfica: Duarte, M., Ferreira da Silva, C., & Moro, S. (2025). Machine learning models to predict the COVID-19 reproduction rate: Combining non-pharmaceutical interventions with sociodemographic and cultural characteristics. Informatics for Health and Social Care, 50(2), 81-99. https://doi.org/10.1080/17538157.2025.2491517
ISSN: 1753-8157
DOI (Digital Object Identifier): 10.1080/17538157.2025.2491517
Palavras-chave: COVID-19
NPIs
Non-pharmaceutical interventions
Hofstede’s cultural dimensions
Machine learning
Reproduction rate Rt
Resumo: Since the beginning of the COVID-19 pandemic, countries worldwide have implemented a set of Non-Pharmaceutical Interventions (NPIs) to prevent the dissemination of the pandemic. Few studies applied machine learning models to compare the use of NPIs, socioeconomic and demographic characteristics, and cultural dimensions in predicting the reproduction rate Rt. We adopted the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology using as data sources the “Our World in Data COVID-19”, the “Oxford COVID-19 Government Response Tracker” and the Hofstede Insights data. We analysed the impact that the Hofstede's cultural dimensions, the implementation of various degrees of restriction of NPIs and the sociodemographic variables may have in the reproduction rate by applying machine learning models to understand whether cultural characteristics are useful information to improve reproduction rate predictions. We included data from 101 countries to train several machine learning models to compare the results between the models with and without the Hofstede's cultural dimensions. Our results show the use of cultural dimensions helps to improve the models, and that the ones that obtained a better prediction of the Rt were the ensemble models, especially the Random Forest.
Arbitragem científica: yes
Acesso: Acesso Embargado
Aparece nas coleções:ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

Ficheiros deste registo:
Ficheiro TamanhoFormato 
article_110630.pdf
  Restricted Access
1,15 MBAdobe PDFVer/Abrir Request a copy


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis Logotipo do Orcid 

Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.