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Open AccessArticle

Covid-19 Predictions Using a Gauss Model, Based on Data from April 2

1
Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
2
Space- and Astrophysics, Ruhr University Bochum, D-44780 Bochum, Germany
3
Institute of Theoretical Physics and Astrophysics, Christian-Albrechts-University Kiel, D-24118 Kiel, Germany
4
Polymer Physics, Department of Materials, ETH Zurich, 8093 Zurich, Switzerland
*
Authors to whom correspondence should be addressed.
This manuscript was submitted to the medRxiv preprint server on April 5, and experienced enormous delay afterwards. We decided to send in the original rather than updated manuscript for a fair assessment of its content.
Physics 2020, 2(2), 197-212; https://doi.org/10.3390/physics2020013
Received: 13 April 2020 / Revised: 16 May 2020 / Accepted: 2 June 2020 / Published: 5 June 2020
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
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 χ 2 -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. View Full-Text
Keywords: statistical methods in physics; health science; extrapolation; parameter estimation; pandemic spreading; virus; forecast; time evolution; dynamics statistical methods in physics; health science; extrapolation; parameter estimation; pandemic spreading; virus; forecast; time evolution; dynamics
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MDPI and ACS Style

Schüttler, J.; Schlickeiser, R.; Schlickeiser, F.; Kröger, M. Covid-19 Predictions Using a Gauss Model, Based on Data from April 2. Physics 2020, 2, 197-212. https://doi.org/10.3390/physics2020013

AMA Style

Schüttler J, Schlickeiser R, Schlickeiser F, Kröger M. Covid-19 Predictions Using a Gauss Model, Based on Data from April 2. Physics. 2020; 2(2):197-212. https://doi.org/10.3390/physics2020013

Chicago/Turabian Style

Schüttler, Janik; Schlickeiser, Reinhard; Schlickeiser, Frank; Kröger, Martin. 2020. "Covid-19 Predictions Using a Gauss Model, Based on Data from April 2" Physics 2, no. 2: 197-212. https://doi.org/10.3390/physics2020013

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