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Comparative and quantitative analysis of COVID-19 epidemic interventions in Chinese provinces.
Liu, Huan; Rong, Zhiwei; Qi, Xinye; Fu, Jinming; Huang, Hao; Cao, Lei; Shan, Linghan; Zhao, Yashuang; Li, Kang; Hao, Yanhua; Jiao, Mingli; Wu, Qunhong; Zhang, Xue.
Afiliação
  • Liu H; Department of Health Policy, Health Management College, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Rong Z; Department of Social Medicine, School of Public Health, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Qi X; Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Fu J; Department of Health Policy, Health Management College, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Huang H; Department of Social Medicine, School of Public Health, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Cao L; Department of Epidemiology, School of Public Health, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Shan L; Department of Epidemiology, School of Public Health, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Zhao Y; Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Li K; Department of Health Policy, Health Management College, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Hao Y; Department of Social Medicine, School of Public Health, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Jiao M; Department of Epidemiology, School of Public Health, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Wu Q; Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Zhang X; Department of Health Policy, Health Management College, Harbin Medical University, Harbin, Heilongjiang Province, China.
Results Phys ; 25: 104305, 2021 Jun.
Article em En | MEDLINE | ID: mdl-34002128
ABSTRACT
A mathematical model was developed to evaluate and compare the effects and intensity of the coronavirus disease 2019 prevention and control measures in Chinese provinces. The time course of the disease with government intervention was described using a dynamic model. The estimated government intervention parameters and area difference between with and without intervention were considered as the intervention intensity and effect, respectively. The model of the disease time course without government intervention predicted that by April 30, 2020, about 3.08% of the population would have been diagnosed with coronavirus disease 2019 in China. Guangdong Province averted the most cases. Comprehensive intervention measures, in which social distancing measures may have played a greater role than isolation measures, resulted in reduced infection cases. Shanghai had the highest intervention intensity. In the context of the global coronavirus disease 2019 pandemic, the prevention and control experience of some key areas in China (such as Shanghai and Guangdong) can provide references for outbreak control in many countries.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Results Phys Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Results Phys Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China