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Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population during the COVID-19 Pandemic

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  • Tiago Tamagusko

    (Research Center for Territory, Transports and Environment, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal)

  • Adelino Ferreira

    (Research Center for Territory, Transports and Environment, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal)

Abstract

SARS-CoV-2 emerged in late 2019. Since then, it has spread to several countries, becoming classified as a pandemic. So far, there is no definitive treatment or vaccine, so the best solution is to prevent transmission between individuals through social distancing. However, it is not easy to measure the effectiveness of these distance measures. Therefore, this study uses data from Google COVID-19 Community Mobility Reports to understand the Portuguese population’s mobility patterns during the COVID-19 pandemic. In this study, the Rt value was modeled for Portugal. In addition, the changepoint was calculated for the population mobility patterns. Thus, the mobility pattern change was used to understand the impact of social distance measures on the dissemination of COVID-19. As a result, it can be stated that the initial Rt value in Portugal was very close to 3, falling to values close to 1 after 25 days. Social isolation measures were adopted quickly. Furthermore, it was observed that public transport was avoided during the pandemic. Finally, until the emergence of a vaccine or an effective treatment, this is the new normal, and it must be understood that new patterns of mobility, social interaction, and hygiene must be adapted to this reality.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9775-:d:449710
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    References listed on IDEAS

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    1. Venter, Zander & Barton, David & gundersen, vegard & Figari, Helene & Nowell, Megan, 2020. "Urban nature in a time of crisis: recreational use of green space increases during the COVID-19 outbreak in Oslo, Norway," SocArXiv kbdum, Center for Open Science.
    2. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
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    1. Zhang, Wenjia & Wu, Yulin & Deng, Guobang, 2024. "Social and spatial disparities in individuals’ mobility response time to COVID-19: A big data analysis incorporating changepoint detection and accelerated failure time models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).
    2. Joao T. Aparicio & Elisabete Arsenio & Rui Henriques, 2021. "Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
    3. Konečný Vladimír & Zuzaniak Martin & Brídziková Mária & Jaśkiewicz Marek, 2023. "Regional Differences in the Impact of the COVID-19 Pandemic on the Demand for Bus Transport in the Slovak Republic," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 146-157, January.
    4. Lukas Hartwig & Reinhard Hössinger & Yusak Octavius Susilo & Astrid Gühnemann, 2022. "The Impacts of a COVID-19 Related Lockdown (and Reopening Phases) on Time Use and Mobility for Activities in Austria—Results from a Multi-Wave Combined Survey," Sustainability, MDPI, vol. 14(12), pages 1-24, June.
    5. 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.
    6. Sreten Simović & Tijana Ivanišević & Bojana Bradić & Svetlana Čičević & Aleksandar Trifunović, 2021. "What Causes Changes in Passenger Behavior in South-East Europe during the COVID-19 Pandemic?," Sustainability, MDPI, vol. 13(15), pages 1-11, July.
    7. Benedek Nagy & Manuela Rozalia Gabor & Ioan Bogdan Baco? & Moaaz Kabil & Kai Zhu & Lóránt Dénes Dávid, 2023. "Google and apple mobility data as predictors for European tourism during the COVID-19 pandemic: A neural network approach," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(2), pages 419-459, June.
    8. Artur Strzelecki & Ana Azevedo & Mariia Rizun & Paulina Rutecka & Kacper Zagała & Karina Cicha & Alexandra Albuquerque, 2022. "Human Mobility Restrictions and COVID-19 Infection Rates: Analysis of Mobility Data and Coronavirus Spread in Poland and Portugal," IJERPH, MDPI, vol. 19(21), pages 1-25, November.

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