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

Author

Listed:
  • Pablo Boullosa

    (Departamento de Física Aplicada, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain)

  • Adrián Garea

    (Departamento de Física Aplicada, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain)

  • Iván Area

    (CITMAga, Departamento de Matemática Aplicada II. E.E. Aeronáutica e do Espazo, Campus de Ourense, Universidade de Vigo, 32003 Ourense, Spain)

  • Juan J. Nieto

    (CITMAga, Instituto de Matemáticas, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain)

  • Jorge Mira

    (Departamento de Física Aplicada, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
    Instituto de Materiais (iMATUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain)

Abstract

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.

Suggested Citation

  • Pablo Boullosa & Adrián Garea & Iván Area & Juan J. Nieto & Jorge Mira, 2022. "Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-15, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2494-:d:865493
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    References listed on IDEAS

    as
    1. Hana M Dobrovolny & Micaela B Reddy & Mohamed A Kamal & Craig R Rayner & Catherine A A Beauchemin, 2013. "Assessing Mathematical Models of Influenza Infections Using Features of the Immune Response," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-20, February.
    2. Nicola Jones, 2020. "How COVID-19 is changing the cold and flu season," Nature, Nature, vol. 588(7838), pages 388-390, December.
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