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1000 Titel
  • Forecasting the novel coronavirus COVID-19
1000 Autor/in
  1. Petropoulos, Fotios |
  2. Makridakis, Spyros |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-03-31
1000 Erschienen in
1000 Quellenangabe
  • 15(3):e0231236
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pone.0231236 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7108716/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • What will be the global impact of the novel coronavirus (COVID-19)? Answering this question requires accurate forecasting the spread of confirmed cases as well as analysis of the number of deaths and recoveries. Forecasting, however, requires ample historical data. At the same time, no prediction is certain as the future rarely repeats itself in the same way as the past. Moreover, forecasts are influenced by the reliability of the data, vested interests, and what variables are being predicted. Also, psychological factors play a significant role in how people perceive and react to the danger from the disease and the fear that it may affect them personally. This paper introduces an objective approach to predicting the continuation of the COVID-19 using a simple, but powerful method to do so. Assuming that the data used is reliable and that the future will continue to follow the past pattern of the disease, our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty. The risks are far from symmetric as underestimating its spread like a pandemic and not doing enough to contain it is much more severe than overspending and being over careful when it will not be needed. This paper describes the timeline of a live forecasting exercise with massive potential implications for planning and decision making and provides objective forecasts for the confirmed cases of COVID-19.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal Decision making
lokal Research validity
lokal China
lokal Fear
lokal SARS
lokal Forecasting
lokal Avian influenza
lokal Social media
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0003-3039-4955|https://orcid.org/0000-0003-0044-6996
1000 Label
1000 Fördernummer
  1. -
1000 Förderprogramm
  1. -
1000 Dateien
  1. Forecasting the novel coronavirus COVID-19
1000 Objektart article
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1000 @id frl:6419711.rdf
1000 Erstellt am 2020-04-06T11:09:48.991+0200
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1000 Zuletzt bearbeitet 2020-04-06T11:11:16.751+0200
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1000 Vgl. frl:6419711
1000 Oai Id
  1. oai:frl.publisso.de:frl:6419711 |
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