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1000 Titel
  • Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases
1000 Autor/in
  1. Jung, Sung-mok |
  2. Akhmetzhanov, Andrei R. |
  3. Hayashi, Katsuma |
  4. Linton, Natalie |
  5. Yang, Yichi |
  6. Yuan, Baoyin |
  7. Kobayashi, Tetsuro |
  8. Nishiura, Hiroshi |
  9. Kinoshita, Ryo |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-02-14
1000 Erschienen in
1000 Quellenangabe
  • 9(2):523
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.3390/jcm9020523 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal emerging infectious diseases
lokal travel
lokal mortality
lokal migration
lokal importation
lokal censoring
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-0787-4515|https://orcid.org/0000-0003-3269-7351|https://frl.publisso.de/adhoc/uri/SGF5YXNoaSwgS2F0c3VtYQ==|https://orcid.org/0000-0002-5464-0076|https://frl.publisso.de/adhoc/uri/WWFuZywgWWljaGk=|https://frl.publisso.de/adhoc/uri/WXVhbiwgQmFveWlu|https://frl.publisso.de/adhoc/uri/S29iYXlhc2hpLCBUZXRzdXJv|https://orcid.org/0000-0003-0941-8537|https://orcid.org/0000-0002-0116-4598
1000 Label
1000 Förderer
  1. Japan Agency for Medical Research and Development |
  2. Japan Society for the Promotion of Science |
  3. Inamori Foundation |
  4. Japan Science and Technology Agency |
  5. Ministry of Education, Culture, Sports, Science and Technology |
1000 Fördernummer
  1. JP18fk0108050
  2. 17H04701; 17H05808, 18H04895, 19H01074; 18J21587
  3. -
  4. JPMJCR1413
  5. -
1000 Förderprogramm
  1. -
  2. KAKENHI - Grants-in-Aid for Scientific Research
  3. -
  4. CREST program
  5. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Japan Agency for Medical Research and Development |
    1000 Förderprogramm -
    1000 Fördernummer JP18fk0108050
  2. 1000 joinedFunding-child
    1000 Förderer Japan Society for the Promotion of Science |
    1000 Förderprogramm KAKENHI - Grants-in-Aid for Scientific Research
    1000 Fördernummer 17H04701; 17H05808, 18H04895, 19H01074; 18J21587
  3. 1000 joinedFunding-child
    1000 Förderer Inamori Foundation |
    1000 Förderprogramm -
    1000 Fördernummer -
  4. 1000 joinedFunding-child
    1000 Förderer Japan Science and Technology Agency |
    1000 Förderprogramm CREST program
    1000 Fördernummer JPMJCR1413
  5. 1000 joinedFunding-child
    1000 Förderer Ministry of Education, Culture, Sports, Science and Technology |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6420355.rdf
1000 Erstellt am 2020-04-23T08:22:06.421+0200
1000 Erstellt von 21
1000 beschreibt frl:6420355
1000 Bearbeitet von 25
1000 Zuletzt bearbeitet 2021-02-12T09:34:12.426+0100
1000 Objekt bearb. Fri Feb 12 09:34:12 CET 2021
1000 Vgl. frl:6420355
1000 Oai Id
  1. oai:frl.publisso.de:frl:6420355 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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