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1000 Titel
  • Outbreak analysis with a logistic growth model shows COVID-19 suppression dynamics in China
1000 Autor/in
  1. Zou, Yi |
  2. Pan, Stephen W |
  3. Zhao, Peng |
  4. Han, Lei |
  5. Wang, Xiaoxiang |
  6. Hemerik, Lia |
  7. Knops, Johannes M H |
  8. van der Werf, Wopke |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-06-29
1000 Erschienen in
1000 Quellenangabe
  • 15(6):e0235247
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pone.0235247 |
1000 Ergänzendes Material
  • https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235247#sec005 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • China reported a major outbreak of a novel coronavirus, SARS-CoV2, from mid-January till mid-March 2020. We review the epidemic virus growth and decline curves in China using a phenomenological logistic growth model to summarize the outbreak dynamics using three parameters that characterize the epidemic’s timing, rate and peak. During the initial phase, the number of virus cases doubled every 2.7 days (range 2.2–4.4 across provinces). The rate of increase in the number of reported cases peaked approximately 10 days after suppression measures were started on 23–25 January 2020. The peak in the number of reported sick cases occurred on average 18 days after the start of suppression measures. From the time of starting measures till the peak, the number of cases increased by a factor 39 in the province Hubei, and by a factor 9.5 for all of China (range: 6.2–20.4 in the other provinces). Complete suppression took up to 2 months (range: 23-57d.), during which period severe restrictions, social distancing measures, testing and isolation of cases were in place. The suppression of the disease in China has been successful, demonstrating that suppression is a viable strategy to contain SARS-CoV2.
1000 Sacherschließung
lokal Epidemiology
gnd 1206347392 COVID-19
lokal Public and occupational health
lokal Infectious disease epidemiology
lokal Vaccines
lokal China
lokal Death rates
lokal Disease dynamics
lokal Hospitalizations
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-7082-9258|https://orcid.org/0000-0003-3612-7508|https://frl.publisso.de/adhoc/uri/WmhhbywgUGVuZw==|https://frl.publisso.de/adhoc/uri/SGFuLCBMZWk=|https://orcid.org/0000-0001-6365-4739|https://orcid.org/0000-0002-6892-2840|https://orcid.org/0000-0002-9647-9209|https://orcid.org/0000-0002-5506-4699
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1000 Erstellt am 2020-06-30T08:08:36.805+0200
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