Boullosa González, PabloGarea Espejo, AdriánArea Carracedo, Iván CarlosNieto Roig, Juan JoséMira Pérez, Jorge2023-02-212023-02-212022Mathematics 2022, 10(14), 2494; https://doi.org/10.3390/math101424942227-7390http://hdl.handle.net/10347/30187The evolution of some epidemics, such as influenza, demonstrates common patterns both in different regions and from year to year. On the contrary, epidemics such as the novel COVID-19 show quite heterogeneous dynamics and are extremely susceptible to the measures taken to mitigate their spread. In this paper, we propose empirical dynamic modeling to predict the evolution of influenza in Spain’s regions. It is a non-parametric method that looks into the past for coincidences with the present to make the forecasts. Here, we extend the method to predict the evolution of other epidemics at any other starting territory and we also test this procedure with Spanish COVID-19 data. We finally build influenza and COVID-19 networks to check possible coincidences in the geographical distribution of both diseases. With this, we grasp the uniqueness of the geographical dynamics of COVID-19eng© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/Non-parametric modelingFluInfluenzaCOVID-19SARS-CoV-2Empirical dynamic modelingForecastingjournal article10.3390/math10142494open access