RT Journal Article
T1 Leveraging geographically distributed data for influenza and SARS-CoV-2 non-parametric forecasting
A1 Boullosa González, Pablo
A1 Garea Espejo, Adrián
A1 Area Carracedo, Iván Carlos
A1 Nieto Roig, Juan José
A1 Mira Pérez, Jorge
K1 Non-parametric modeling
K1 Flu
K1 Influenza
K1 COVID-19
K1 SARS-CoV-2
K1 Empirical dynamic modeling
K1 Forecasting
AB The evolution of some epidemics, such as influenza, demonstrates common patterns bothin 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 geographicaldistribution of both diseases. With this, we grasp the uniqueness of the geographical dynamics of COVID-19
PB MDPI
SN 2227-7390
YR 2022
FD 2022
LK http://hdl.handle.net/10347/30187
UL http://hdl.handle.net/10347/30187
LA eng
NO Mathematics 2022, 10(14), 2494; https://doi.org/10.3390/math10142494
NO This research was supported by the Instituto de Salud Carlos III, within the Project COV20/00617 in the scope of the “Fondo COVID” of the Ministerio de Ciencia e Innovación of Spain, and by the crowdfunding program “Sumo Valor” of the University of Santiago de Compostela. Area and Nieto have been partially supported by the Agencia Estatal de Investigación (AEI) of Spain under Grant PID2020-113275GB-I00, cofinanced by the European Community fund FEDER. Mira is part of iMATUS, supported by Xunta de Galicia
DS Minerva
RD 22 abr 2026