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Covid-19 Predictions Using a Gauss Model, Based on Data from April 2
: Received: 10 April 2020 / Approved: 11 April 2020 / Online: 11 April 2020 (01:25:26 CEST)
A peer-reviewed article of this Preprint also exists.
Journal reference: Physics 2020, 2, 197-212
We propose a Gauss model (GM), a map from time to the bell-shaped Gauss function to model the deaths per day and country, as a quick and simple model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a pandemic, i.e. initial exponential spread to eventual saturation, we apply the GM to existing data, as of April 2, 2020, from 25 countries during first corona pandemic wave and study the model's predictions. We find that logarithmic daily fatalities caused by Covid-19 are well described by a quadratic function in time. By fitting the data to second order polynomials from a statistical chi2-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.
Supplementary and Associated Material
coronavirus; statistical analysis; extrapolation; parameter estimation; pandemic spreading; virus; forecast; time evolution; dynamics
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