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A Spatial-Temporal Model for the Evolution of the COVID-19 Pandemic in Spain Including Mobility

Author

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  • Francesc Aràndiga

    (Departament de Matemàtiques, Universitat de València, Av. Vicent Andrés Estellés, E-46100 Burjassot, Spain)

  • Antonio Baeza

    (Departament de Matemàtiques, Universitat de València, Av. Vicent Andrés Estellés, E-46100 Burjassot, Spain)

  • Isabel Cordero-Carrión

    (Departament de Matemàtiques, Universitat de València, Av. Vicent Andrés Estellés, E-46100 Burjassot, Spain)

  • Rosa Donat

    (Departament de Matemàtiques, Universitat de València, Av. Vicent Andrés Estellés, E-46100 Burjassot, Spain)

  • M. Carmen Martí

    (Departament de Matemàtiques, Universitat de València, Av. Vicent Andrés Estellés, E-46100 Burjassot, Spain)

  • Pep Mulet

    (Departament de Matemàtiques, Universitat de València, Av. Vicent Andrés Estellés, E-46100 Burjassot, Spain)

  • Dionisio F. Yáñez

    (Departament de Matemàtiques, Universitat de València, Av. Vicent Andrés Estellés, E-46100 Burjassot, Spain)

Abstract

In this work, a model for the simulation of infectious disease outbreaks including mobility data is presented. The model is based on the SAIR compartmental model and includes mobility data terms that model the flow of people between different regions. The aim of the model is to analyze the influence of mobility on the evolution of a disease after a lockdown period and to study the appearance of small epidemic outbreaks due to the so-called imported cases . We apply the model to the simulation of the COVID-19 in the various areas of Spain, for which the authorities made available mobility data based on the position of cell phones. We also introduce a method for the estimation of incomplete mobility data. Some numerical experiments show the importance of data completion and indicate that the model is able to qualitatively simulate the spread tendencies of small outbreaks. This work was motivated by an open call made to the mathematical community in Spain to help predict the spread of the epidemic.

Suggested Citation

  • Francesc Aràndiga & Antonio Baeza & Isabel Cordero-Carrión & Rosa Donat & M. Carmen Martí & Pep Mulet & Dionisio F. Yáñez, 2020. "A Spatial-Temporal Model for the Evolution of the COVID-19 Pandemic in Spain Including Mobility," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1677-:d:422468
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    References listed on IDEAS

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    1. Roman Wölfel & Victor M. Corman & Wolfgang Guggemos & Michael Seilmaier & Sabine Zange & Marcel A. Müller & Daniela Niemeyer & Terry C. Jones & Patrick Vollmar & Camilla Rothe & Michael Hoelscher & To, 2020. "Author Correction: Virological assessment of hospitalized patients with COVID-2019," Nature, Nature, vol. 588(7839), pages 35-35, December.
    2. Pedro S Peixoto & Diego Marcondes & Cláudia Peixoto & Sérgio M Oliva, 2020. "Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-23, July.
    3. Roman Wölfel & Victor M. Corman & Wolfgang Guggemos & Michael Seilmaier & Sabine Zange & Marcel A. Müller & Daniela Niemeyer & Terry C. Jones & Patrick Vollmar & Camilla Rothe & Michael Hoelscher & To, 2020. "Virological assessment of hospitalized patients with COVID-2019," Nature, Nature, vol. 581(7809), pages 465-469, May.
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    Cited by:

    1. Zhaofu Hong & Yingjie Li & Yeming Gong & Wanying Chen, 2024. "A data-driven spatially-specific vaccine allocation framework for COVID-19," Annals of Operations Research, Springer, vol. 339(1), pages 203-226, August.

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