https://socictopen.socict.org/files/original/e7dd17141fb65bbfe9ab5360d51149b5.pdf cb92232d8f04f042343e7b4bde918bda Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Covid-19 Predictions Using a Gauss Model, Based on Data from April 2 Creator An entity primarily responsible for making the resource Martin Kröger, Reinhard Schlickeiser, Frank Schlickeiser, Janik Schüttler Description An account of the resource 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. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource virus, extrapolation, parameter estimation, Health science, statistical methods in physics, pandemic spreading Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/physics2020013 Source A related resource from which the described resource is derived Physics Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Physics