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Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence.
Wang, Ligui; Lin, Mengxuan; Wang, Jiaojiao; Chen, Hui; Yang, Mingjuan; Qiu, Shaofu; Zheng, Tao; Li, Zhenjun; Song, Hongbin.
Afiliação
  • Wang L; Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Chinese People's Liberation Army, Beijing, China.
  • Lin M; Academy of Military Medical Sciences, Academy of Military Science of Chinese PLA, Beijing, China.
  • Wang J; The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Chen H; Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Chinese People's Liberation Army, Beijing, China.
  • Yang M; Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Chinese People's Liberation Army, Beijing, China.
  • Qiu S; Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Chinese People's Liberation Army, Beijing, China.
  • Zheng T; Academy of Military Medical Sciences, Academy of Military Science of Chinese PLA, Beijing, China.
  • Li Z; State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
  • Song H; Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Chinese People's Liberation Army, Beijing, China.
Infect Dis Model ; 7(2): 117-126, 2022 Jun.
Article em En | MEDLINE | ID: mdl-35475256
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prevalence_studies / Risk_factors_studies Idioma: En Revista: Infect Dis Model Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prevalence_studies / Risk_factors_studies Idioma: En Revista: Infect Dis Model Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China