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The Enlightenment of mainland China's epidemic situation to the world through the intrinsic growth rule of infected and cured cases with COVID-19
Chuanliang Han; Yimeng Liu; Jiting Tang; Yuyao Zhu; Carlo Jaeger; Saini Yang.
Afiliação
  • Chuanliang Han; Beijing Normal University
  • Yimeng Liu; Beijing Normal University
  • Jiting Tang; Beijing Normal University
  • Yuyao Zhu; Beijing Normal University
  • Carlo Jaeger; Beijing Normal University
  • Saini Yang; Beijing Normal University
Preprint em En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20067454
ABSTRACT
The novel coronavirus disease (COVID-19) that emerged at the end of 2019 has been controlled in mainland China so far, while it is still spreading globally. When the pandemic will end is a question of great concern. A logistic model depicting the growth rules of infected and recovered cases in mainland China may shed some light on this question. We extended this model to 31 countries outside China experiencing serious COVID-2019 outbreaks. The model well explained the data in our study (R2 [≥] 0.95). For infected cases, the semi-saturation period (SSP) ranges from 63 to 170 days (March 3 to June 18). The logistic growth rate of infected cases is positively correlated with that of recovered cases, and the same holds for the SSP. According to the linear connection between the growth rules for infected and recovered cases identified from the Chinese data, we predicted that the SSP of the recovered cases outside China ranges from 82 to 196 days (March 22 to July 8). More importantly, we found a strong positive correlation between the SSP of infected cases and the timing of governments response, providing strong evidence for the effectiveness of rapid epidemic control measures in various countries.
Licença
cc_by_nc_nd
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-MEDRXIV Tipo de estudo: Experimental_studies / Observational_studies / Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-MEDRXIV Tipo de estudo: Experimental_studies / Observational_studies / Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Preprint