journal.pone.0230405.pdf 838,12KB
1000 Titel
  • Data-based analysis, modelling and forecasting of the COVID-19 outbreak
1000 Autor/in
  1. Anastassopoulou, Cleo |
  2. Russo, Lucia |
  3. Tsakris, Athanasios |
  4. Siettos, Constantinos |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-03-31
1000 Erschienen in
1000 Quellenangabe
  • 15(3): e0230405
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
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1000 Ergänzendes Material
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1000 Publikationsstatus
1000 Begutachtungsstatus
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1000 Abstract/Summary
  • Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infectious-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ∼4.6, while the one computed under the second scenario was found to be ∼3.2. Thus, based on the SIRD simulations, the estimated average value of R0 was found to be ∼2.6 based on confirmed cases and ∼2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with a lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around ∼0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal Epidemology
lokal Infectious disease epidemology
lokal Mathematical functions
lokal Simulation and modeling
lokal Respiratory infections
lokal China
lokal Linear regression analysis
lokal Death rates
1000 Fächerklassifikation (DDC)
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1000 Dateien
  1. Data-based analysis, modelling and forecasting of the COVID-19 outbreak
1000 Objektart article
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1000 @id frl:6419718.rdf
1000 Erstellt am 2020-04-06T11:21:01.869+0200
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