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Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data

Author

Listed:
  • Nelson Mileu

    (Portugal and Associated Laboratory Terra, Institute of Geography and Spatial Planning, Centre of Geographical Studies, University of Lisbon, 1600-276 Lisbon, Portugal)

  • Nuno M. Costa

    (Portugal and Associated Laboratory Terra, Institute of Geography and Spatial Planning, Centre of Geographical Studies, University of Lisbon, 1600-276 Lisbon, Portugal)

  • Eduarda M. Costa

    (Portugal and Associated Laboratory Terra, Institute of Geography and Spatial Planning, Centre of Geographical Studies, University of Lisbon, 1600-276 Lisbon, Portugal)

  • André Alves

    (Directorate-General for Territory, 1099-052 Lisbon, Portugal)

Abstract

The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobility.

Suggested Citation

  • Nelson Mileu & Nuno M. Costa & Eduarda M. Costa & André Alves, 2022. "Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data," Data, MDPI, vol. 7(8), pages 1-17, July.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:8:p:107-:d:877199
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    References listed on IDEAS

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    4. Tiago Tamagusko & Adelino Ferreira, 2020. "Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population during the COVID-19 Pandemic," Sustainability, MDPI, vol. 12(22), pages 1-12, November.
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    Cited by:

    1. Bulut Boru & M. Emre Gursoy, 2022. "Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics," Data, MDPI, vol. 7(11), pages 1-24, November.

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