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Article

Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting

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Departamento de Física Aplicada, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
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CITMAga, Departamento de Matemática Aplicada II. E.E. Aeronáutica e do Espazo, Campus de Ourense, Universidade de Vigo, 32003 Ourense, Spain
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CITMAga, Instituto de Matemáticas, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
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Instituto de Materiais (iMATUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Ricardo Lopez-Ruiz
Mathematics 2022, 10(14), 2494; https://doi.org/10.3390/math10142494
Received: 1 June 2022 / Revised: 13 July 2022 / Accepted: 14 July 2022 / Published: 18 July 2022
(This article belongs to the Special Issue Mathematical Biology: Modeling, Analysis, and Simulations II)
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. View Full-Text
Keywords: non-parametric modeling; flu; influenza; COVID-19; SARS-CoV-2; empirical dynamic modeling; forecasting non-parametric modeling; flu; influenza; COVID-19; SARS-CoV-2; empirical dynamic modeling; forecasting
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MDPI and ACS Style

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. https://doi.org/10.3390/math10142494

AMA Style

Boullosa P, Garea A, Area I, Nieto JJ, Mira J. Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting. Mathematics. 2022; 10(14):2494. https://doi.org/10.3390/math10142494

Chicago/Turabian Style

Boullosa, Pablo, Adrián Garea, Iván Area, Juan J. Nieto, and Jorge Mira. 2022. "Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting" Mathematics 10, no. 14: 2494. https://doi.org/10.3390/math10142494

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