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 such as the novel COVID-19 show quite heterogeneous dynamics and are extremely susceptible to the measures taken to mitigate their spread. In t...

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Bibliographic Details
Published in:Mathematics (Basel) 2022-07, Vol.10 (14), p.2494
Main Authors: Boullosa, Pablo, Garea, Adrián, Area, Iván, Nieto, Juan J., Mira, Jorge
Format: Article
Language:English
Subjects:
COVID-19
Dynamic models
empirical dynamic modeling
Epidemics
Evolution
flu
Geographical distribution
Influenza
Mathematics
non-parametric modeling
Nonparametric statistics
Pandemics
SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10142494