Covid-19 predictions using a Gauss model, based on data from April 2

Janik Schuttler; Reinhard Schlickeiser; Frank Schlickeiser; Martin Kroger

First, let us list some other 'microscopic' models that can lead to a Gaussian dynamics of fatalities or infections. One of more prominent ones recently appeared in the Washington Post [14] . Stevens investigated what happens when simulitis spreads in a town, if everyone in the town starts at a random position, moving at a random angle, infecting others upon collision, and recovering after a certain time. The simulated number of infected people rises rapidly as the disease spreads and tapers off as people recover -a bell-shaped curve. We recreated the simulations and found evidence for the applicability of the GM under many circumstances. These results are not reported here, but support our central assumption. From another recent work using a holistic agent-based model [3] , where the agents adapt their behavior through artificial intelligence as part of the solution, there seems also evidence from the numerical results presented, that the number of newly infected may be well captured by a Gaussian function.