An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic.

Pourroostaei Ardakani, Saeid; Xia, Tianqi; Cheshmehzangi, Ali; Zhang, Zhiang

Pourroostaei Ardakani, Saeid; Xia, Tianqi; Cheshmehzangi, Ali; Zhang, Zhiang.

Afiliação

Pourroostaei Ardakani S; Department of Computer Science, University of Nottingham, Ningbo, 315100 China.
Xia T; Department of Computer Science, University of Nottingham, Ningbo, 315100 China.
Cheshmehzangi A; Department of Architecture and Built Environment, University of Nottingham, Ningbo, 315100 China.
Zhang Z; Department of Architecture and Built Environment, University of Nottingham, Ningbo, 315100 China.

Genus ; 78(1): 28, 2022.

Article em En | MEDLINE | ID: mdl-36090535

Palavras-chave

COVID-19; Lockdown; Machine learning; Predictive analysis

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prevalence_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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