期刊
MATHEMATICS
卷 10, 期 14, 页码 -出版社
MDPI
DOI: 10.3390/math10142494
关键词
non-parametric modeling; flu; influenza; COVID-19; SARS-CoV-2; empirical dynamic modeling; forecasting
类别
资金
- Instituto de Salud Carlos III [COV20/00617]
- Agencia Estatal de Investigacion (AEI) of Spain [PID2020-113275GB-I00]
- European Community fund FEDER
- Xunta de Galicia
- crowdfunding program Sumo Valor of the University of Santiago de Compostela
This paper proposes an empirical dynamic modeling method to predict the evolution of influenza in different regions and extends it to predict other epidemics. The researchers also investigate the geographical distribution of influenza and COVID-19 through network analysis.
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.
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