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1000 Titel
  • Estimation of time-varying reproduction numbers underlying epidemiological processes: A new statistical tool for the COVID-19 pandemic
1000 Autor/in
  1. Hong, Hyokyoung |
  2. Li, Yi |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-07-21
1000 Erschienen in
1000 Quellenangabe
  • 15(7):e0236464
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pone.0236464 |
1000 Ergänzendes Material
  • https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236464#sec012 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • The coronavirus pandemic has rapidly evolved into an unprecedented crisis. The susceptible-infectious-removed (SIR) model and its variants have been used for modeling the pandemic. However, time-independent parameters in the classical models may not capture the dynamic transmission and removal processes, governed by virus containment strategies taken at various phases of the epidemic. Moreover, few models account for possible inaccuracies of the reported cases. We propose a Poisson model with time-dependent transmission and removal rates to account for possible random errors in reporting and estimate a time-dependent disease reproduction number, which may reflect the effectiveness of virus control strategies. We apply our method to study the pandemic in several severely impacted countries, and analyze and forecast the evolving spread of the coronavirus. We have developed an interactive web application to facilitate readers’ use of our method.
1000 Sacherschließung
lokal Global health
lokal Epidemiology
lokal Web-based applications
gnd 1206347392 COVID-19
lokal Infectious disease epidemiology
lokal Respiratory infections
lokal China
lokal Pandemics
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-4280-6243|https://orcid.org/0000-0003-1720-2760
1000 Label
1000 Förderer
  1. National Science Foundation |
  2. National Institutes of Health |
1000 Fördernummer
  1. DMS-1915099
  2. R01AG056764
1000 Förderprogramm
  1. -
  2. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer National Science Foundation |
    1000 Förderprogramm -
    1000 Fördernummer DMS-1915099
  2. 1000 joinedFunding-child
    1000 Förderer National Institutes of Health |
    1000 Förderprogramm -
    1000 Fördernummer R01AG056764
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6422106.rdf
1000 Erstellt am 2020-07-23T11:58:28.868+0200
1000 Erstellt von 122
1000 beschreibt frl:6422106
1000 Bearbeitet von 122
1000 Zuletzt bearbeitet Thu Jul 23 11:59:59 CEST 2020
1000 Objekt bearb. Thu Jul 23 11:59:34 CEST 2020
1000 Vgl. frl:6422106
1000 Oai Id
  1. oai:frl.publisso.de:frl:6422106 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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