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Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study.
Wang, Zengmiao; Wu, Peiyi; Wang, Lin; Li, Bingying; Liu, Yonghong; Ge, Yuxi; Wang, Ruixue; Wang, Ligui; Tan, Hua; Wu, Chieh-Hsi; Laine, Marko; Salje, Henrik; Song, Hongbin.
Afiliación
  • Wang Z; State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
  • Wu P; State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
  • Wang L; Department of Genetics, University of Cambridge, Cambridge, United Kingdom.
  • Li B; State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
  • Liu Y; Beijing Center for Disease Prevention and Control, Beijing, China.
  • Ge Y; State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
  • Wang R; State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
  • Wang L; Center of Disease Control and Prevention, PLA, Beijing, China.
  • Tan H; Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
  • Wu CH; Mathematical Sciences, University of Southampton, Southampton, United Kingdom.
  • Laine M; Finnish Meteorological Institute, Meteorological Research Unit, Helsinki, Finland.
  • Salje H; Department of Genetics, University of Cambridge, Cambridge, United Kingdom.
  • Song H; Center of Disease Control and Prevention, PLA, Beijing, China.
PLoS Comput Biol ; 19(9): e1011492, 2023 09.
Article en En | MEDLINE | ID: mdl-37721947
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Epidemias / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: China
Texto completo
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Epidemias / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: China