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Transmission dynamics and control measures of COVID-19 outbreak in China: a modelling study
XuSheng Zhang; Emilia Vynnycky; Andre Charlett; Daniela de Angelis; Zhengji Chen; Wei Liu.
Affiliation
  • XuSheng Zhang; Public Health England
  • Emilia Vynnycky; Public Health England
  • Andre Charlett; Public Health England
  • Daniela de Angelis; Cambridge University
  • Zhengji Chen; Kunming medical university
  • Wei Liu; Kunming medical University
Preprint in English | medRxiv | ID: ppmedrxiv-20150086
Journal article
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ABSTRACT
COVID-19 is reported to have been effectively brought under control in China at its initial start place. To understand the COVID-19 outbreak in China and provide potential lessons for other parts of the world, in this study we combine a mathematical modelling with multiple datasets to estimate its transmissibility and severity and how it was affected by the unprecedented control measures. Our analyses show that before 29th January 2020, the ascertainment rate is 6.9%(95%CI 3.5 - 14.6%); then it increased to 41.5%(95%CI 30.6 - 65.1%). The basic reproduction number (R0) was 2.23(95%CI 1.86 - 3.22) before 8th February 2020; then it dropped to 0.04(95%CI 0.01 - 0.10). This estimation also indicates that the effect on transmissibility of control measures taken since 23rd January 2020 emerged about two weeks late. The confirmed case fatality rate is estimated at 4.41%(95%CI 3.65 - 5.30%). This shows that SARS-CoV-2 virus is highly transmissible but less severe than SARS-CoV-1 and MERS-CoV. We found that at the early stage, the majority of R0 comes from the undetected infected people. This implies that the successful control in China was achieved through decreasing the contact rates among people in general populations and increasing the rate of detection and quarantine of the infected cases.
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Observational study / Prognostic study Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Observational study / Prognostic study Language: English Year: 2020 Document type: Preprint
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