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 1206.02 Ecuaciones Diferenciales
K1 1206.12 Ecuaciones Diferenciales Ordinarias
K1 2420.08 Virus Respiratorios
AB The 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-19.
PB Mathematics
SN 22277390
YR 2022
FD 2022-07-18
LK http://hdl.handle.net/11093/5543
UL http://hdl.handle.net/11093/5543
LA eng
NO Mathematics, 10(14): 2494 (2022)
NO Xunta de Galicia
DS Investigo
RD 11-dic-2024