Boullosa, P.; Garea, A.; Area, I.; Nieto, J.J.; Mira, J. Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting. Mathematics2022, 10, 2494.
Boullosa, P.; Garea, A.; Area, I.; Nieto, J.J.; Mira, J. Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting. Mathematics 2022, 10, 2494.
Boullosa, P.; Garea, A.; Area, I.; Nieto, J.J.; Mira, J. Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting. Mathematics2022, 10, 2494.
Boullosa, P.; Garea, A.; Area, I.; Nieto, J.J.; Mira, J. Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting. Mathematics 2022, 10, 2494.
Abstract
The evolution of some epidemics, as influenza, shows common patterns both in different regions and from year to year. On the contrary, epidemics like 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 test also 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-19.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
