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Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease

Author

Listed:
  • Javier Del-Águila-Mejía

    (Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid. C. Arzobispo Morcillo 4, 28029 Madrid, Spain
    Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
    Servicio de Medicina Preventiva, Hospital Universitario de Móstoles, Calle Río Júcar s/n, 28935 Móstoles, Spain)

  • David García-García

    (Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
    Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain)

  • Ayelén Rojas-Benedicto

    (Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
    Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
    Universidad Nacional de Educación a Distancia (UNED), Calle de Bravo Murillo 38, 28015 Madrid, Spain)

  • Nicolás Rosillo

    (Servicio de Medicina Preventiva, Hospital Universitario 12 de Octubre, Avenida de Córdoba s/n, 28041 Madrid, Spain)

  • María Guerrero-Vadillo

    (Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
    Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain)

  • Marina Peñuelas

    (Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
    Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain)

  • Rebeca Ramis

    (Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
    Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain)

  • Diana Gómez-Barroso

    (Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
    Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain)

  • Juan de Mata Donado-Campos

    (Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid. C. Arzobispo Morcillo 4, 28029 Madrid, Spain
    Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain)

Abstract

Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.

Suggested Citation

  • Javier Del-Águila-Mejía & David García-García & Ayelén Rojas-Benedicto & Nicolás Rosillo & María Guerrero-Vadillo & Marina Peñuelas & Rebeca Ramis & Diana Gómez-Barroso & Juan de Mata Donado-Campos, 2023. "Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease," IJERPH, MDPI, vol. 20(5), pages 1-15, February.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4356-:d:1083854
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    References listed on IDEAS

    as
    1. Stephen M. Kissler & Nishant Kishore & Malavika Prabhu & Dena Goffman & Yaakov Beilin & Ruth Landau & Cynthia Gyamfi-Bannerman & Brian T. Bateman & Jon Snyder & Armin S. Razavi & Daniel Katz & Jonatha, 2020. "Reductions in commuting mobility correlate with geographic differences in SARS-CoV-2 prevalence in New York City," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    2. Serina Chang & Emma Pierson & Pang Wei Koh & Jaline Gerardin & Beth Redbird & David Grusky & Jure Leskovec, 2021. "Mobility network models of COVID-19 explain inequities and inform reopening," Nature, Nature, vol. 589(7840), pages 82-87, January.
    3. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
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