Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting

The evolution of some epidemics, such as influenza, demonstrates common patterns both in different regions and from year to year. On the contrary, epidemics novel COVID-19 show quite heterogeneous dynamics are extremely susceptible measures taken mitigate their spread. In this paper, we propose empirical dynamic modeling predict influenza Spain’s regions. It is a non-parametric method that looks into past for coincidences with present make forecasts. Here, extend other at any starting territory also test procedure Spanish data. We finally build networks check possible geographical distribution diseases. With this, grasp uniqueness COVID-19.