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1000 Titel
  • Analysis and prediction of the coronavirus disease epidemic in China based on an individual-based model
1000 Autor/in
  1. Guo, Zuiyuan |
  2. Xiao, Dan |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-12-17
1000 Erschienen in
1000 Quellenangabe
  • 10:22123
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1038/s41598-020-76969-4 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7747602/ |
1000 Ergänzendes Material
  • https://www.nature.com/articles/s41598-020-76969-4#Sec20 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • We established a stochastic individual-based model and simulated the whole process of occurrence, development, and control of the coronavirus disease epidemic and the infectors and patients leaving Hubei Province before the traffic was closed in China. Additionally, the basic reproduction number (R0) and number of infectors and patients who left Hubei were estimated using the coordinate descent algorithm. The median R0 at the initial stage of the epidemic was 4.97 (95% confidence interval [CI] 4.82–5.17). Before the traffic lockdown was implemented in Hubei, 2000 (95% CI 1982–2030) infectors and patients had left Hubei and traveled throughout the country. The model estimated that if the government had taken prevention and control measures 1 day later, the cumulative number of laboratory-confirmed patients in the whole country would have increased by 32.1%. If the lockdown of Hubei was imposed 1 day in advance, the cumulative number of laboratory-confirmed patients in other provinces would have decreased by 7.7%. The stochastic model could fit the officially issued data well and simulate the evolution process of the epidemic. The intervention measurements nationwide have effectively curbed the human-to-human transmission of severe acute respiratory syndrome coronavirus 2.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal Infectious diseases
lokal Mathematics and computing
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/R3VvLCBadWl5dWFu|https://frl.publisso.de/adhoc/uri/WGlhbywgRGFu
1000 Label
1000 Förderer
  1. National Science and Technology Major Project |
1000 Fördernummer
  1. 2018ZX10713003
1000 Förderprogramm
  1. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer National Science and Technology Major Project |
    1000 Förderprogramm -
    1000 Fördernummer 2018ZX10713003
1000 Objektart article
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1000 @id frl:6425736.rdf
1000 Erstellt am 2021-02-18T10:28:27.121+0100
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1000 Bearbeitet von 25
1000 Zuletzt bearbeitet Thu Mar 11 07:39:20 CET 2021
1000 Objekt bearb. Thu Mar 11 07:39:10 CET 2021
1000 Vgl. frl:6425736
1000 Oai Id
  1. oai:frl.publisso.de:frl:6425736 |
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