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dc.contributor.author
Schüttler, Janik
dc.contributor.author
Schlickeiser, Reinhard
dc.contributor.author
Schlickeiser, Frank
dc.contributor.author
Kröger, Martin
dc.date.accessioned
2020-07-10T08:06:26Z
dc.date.available
2020-07-04T15:25:15Z
dc.date.available
2020-07-10T08:06:26Z
dc.date.issued
2020-06
dc.identifier.issn
2624-8174
dc.identifier.other
10.3390/physics2020013
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/424412
dc.identifier.doi
10.3929/ethz-b-000424412
dc.description.abstract
We study a Gauss model (GM), a map from time to the bell-shaped Gaussian function to model the deaths per day and country, as a simple, analytically tractable model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a pandemic, i.e., initial exponential spread to eventual saturation, and an agent-based model, we apply the GM to existing data, as of 2 April 2020, from 25 countries during first corona pandemic wave and study the model’s predictions. We find that logarithmic daily fatalities caused by the coronavirus disease 2019 (Covid-19) are well described by a quadratic function in time. By fitting the data to second order polynomials from a statistical χ2 -fit with 95% confidence, we are able to obtain the characteristic parameters of the GM, i.e., a width, peak height, and time of peak, for each country separately, with which we extrapolate to future times to make predictions. We provide evidence that this supposedly oversimplifying model might still have predictive power and use it to forecast the further course of the fatalities caused by Covid-19 per country, including peak number of deaths per day, date of peak, and duration within most deaths occur. While our main goal is to present the general idea of the simple modeling process using GMs, we also describe possible estimates for the number of required respiratory machines and the duration left until the number of infected will be significantly reduced
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Statistical methods in physics
en_US
dc.subject
Health science
en_US
dc.subject
Extrapolation
en_US
dc.subject
Parameter estimation
en_US
dc.subject
Pandemic spreading
en_US
dc.subject
Virus
en_US
dc.subject
Forecast
en_US
dc.subject
Time evolution
en_US
dc.subject
Dynamics
en_US
dc.title
Covid-19 Predictions Using a Gauss Model, Based on Data from April 2
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Physics
ethz.journal.volume
2
en_US
ethz.journal.issue
2
en_US
ethz.pages.start
197
en_US
ethz.pages.end
212
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.publication.place
Basel
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ethz.publication.status
published
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ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02160 - Dep. Materialwissenschaft / Dep. of Materials::02646 - Institut für Polymere / Institute of Polymers::03359 - Oettinger, Christian / Oettinger, Christian
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02160 - Dep. Materialwissenschaft / Dep. of Materials::02646 - Institut für Polymere / Institute of Polymers::03359 - Oettinger, Christian / Oettinger, Christian
en_US
ethz.date.deposited
2020-07-04T15:25:25Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-07-10T08:06:36Z
ethz.rosetta.lastUpdated
2022-03-29T02:37:37Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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