Analysis of the impact of the COVID-19 pandemic on mobility in Brazil from a semantic perspective

• Germano B. dos Santos UFV
• Fabrício A. Silva UFV
• Thais R. M. Braga Silva UFV

DOI: https://doi.org/10.5753/courb.2024.3276

Abstract

In 2020, the SARS-CoV-2 spread quickly in Brazil, implicating on non-pharmaceutical interventions such as social distancing. In this context, the mobility analysis was important to evaluate the infection rates of Brazilian people. Nevertheless, the possible effects on mobility patterns due to the pandemic restrictions during this period are often overlooked. In this context, it is analyzed 95,522,812 mobility records of 65,402 mobile users, regarding the years 2021 and 2022 aiming at understanding how the Brazilian people adapted to the restrictions easing under a semantic view of human mobility. Therefore, using the comparison semantic motif matrix, the analysis of users’ clusters based on embeddings of semantic motifs, and mobility metrics, it is observed that there is a gradual change from teleworking to hybrid models, increasing the displacements’ variability of Brazilian people from 2021 to 2022.

References

Barbosa, H., Barthelemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., Menezes, R., Ramasco, J. J., Simini, F., and Tomasini, M. (2018). Human mobility: Models and applications. Physics Reports, 734:1–74.

Benita, F. (2021). Human mobility behavior in covid-19: A systematic literature review and bibliometric analysis. Sustainable Cities and Society, 70:102916.

Bouzaghrane, M. A., Obeid, H., González, M., and Walker, J. (2024). Human mobility reshaped? deciphering the impacts of the covid-19 pandemic on activity patterns, spatial habits, and schedule habits. EPJ Data Science, 13(1):1–20.

Boz, H. A., Bahrami, M., Balcisoy, S., Bozkaya, B., Mazar, N., Nichols, A., and Pentland, A. (2024). Investigating neighborhood adaptability using mobility networks: a case study of the covid-19 pandemic. Humanities and Social Sciences Communications, 11(1):1–11.

Cao, J., Li, Q., Tu, W., and Wang, F. (2019). Characterizing preferred motif choices and distance impacts. Plos one, 14(4):e0215242.

Capanema, C. G., Silva, F. A., and Silva, T. R. M. (2019). Identificação e classificação de pontos de interesse individuais com base em dados esparsos. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 15–28. SBC.

Chagas, E. T. C., Barros, P. H., Cardoso-Pereira, I., Ponte, I. V., Ximenes, P., Figueiredo, F., Murai, F., Couto da Silva, A. P., Almeida, J. M., Loureiro, A. A. F., and Ramos, H. S. (2021). Effects of population mobility on the covid-19 spread in brazil. PLOS ONE, 16(12):1–27.

Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore y Piontti, A., Mu, K., Rossi, L., Sun, K., et al. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (covid-19) outbreak. Science, 368(6489):395–400.

Dias, E., Diniz, A. M., Souto, G. R., Guerra, H. L., Marques-Neto, H. T., Malinowski, S., and Guimarães, S. J. F. (2024). Predicting covid-19 cases in belo horizonte—brazil taking into account mobility and vaccination issues. Plos one, 19(2):e0269515.

Fanticelli, H. C., Rabenjamina, S., Viana, A. C., Stanica, R., De Oliveira, L. S., and Ziviani, A. (2022). Data-driven mobility analysis and modeling: Typical and confined life of a metropolitan population. 8(3).

Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. (2008). Understanding individual human mobility patterns. nature, 453(7196):779–782.

Iio, K., Guo, X., Kong, X., Rees, K., and Wang, X. B. (2021). Covid-19 and social distancing: Disparities in mobility adaptation between income groups. Transportation Research Interdisciplinary Perspectives, 10:100333.

Le, Q. and Mikolov, T. (2014). Distributed representations of sentences and documents. In International conference on machine learning, pages 1188–1196. PMLR.

Li, A., Zhao, P., Haitao, H., Mansourian, A., and Axhausen, K. W. (2021). How did micro-mobility change in response to covid-19 pandemic? a case study based on spatial-temporal-semantic analytics. Computers, environment and urban systems, 90:101703.

Ma, J., Li, B., and Mostafavi, A. (2023). Characterizing urban lifestyle signatures using motif properties in network of places. Environment and Planning B: Urban Analytics and City Science, page 23998083231206171.

Montoliu, R., Blom, J., and Gatica-Perez, D. (2013). Discovering places of interest in everyday life from smartphone data. Multimedia tools and applications, 62(1):179–207.

Nello-Deakin, S., Diaz, A. B., Roig-Costa, O., Miralles-Guasch, C., and Marquet, O. (2024). Moving beyond covid-19: Break or continuity in the urban mobility regime? Transportation Research Interdisciplinary Perspectives, 24:101060.

Nepomuceno, T. C. C., Garcez, T. V., Silva, L., and Coutinho, A. P. (2022). Measuring the mobility impact on the covid-19 pandemic. Mathematical Biosciences and Engineering, 19(7):7032–7054.

Nouvellet, P., Bhatia, S., Cori, A., Ainslie, K. E., Baguelin, M., Bhatt, S., Boonyasiri, A., Brazeau, N. F., Cattarino, L., Cooper, L. V., et al. (2021). Reduction in mobility and covid-19 transmission. Nature communications, 12(1):1–9.

Pappalardo, L., Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., and Barabási, A.-L. (2015). Returners and explorers dichotomy in human mobility. Nature communications, 6(1):1–8.

Pappalardo, L., Vanhoof, M., Gabrielli, L., Smoreda, Z., Pedreschi, D., and Giannotti, F. (2016). An analytical framework to nowcast well-being using mobile phone data. International Journal of Data Science and Analytics, 2(1):75–92.

Rowe, F., Calafiore, A., Arribas-Bel, D., Samardzhiev, K., and Fleischmann, M. (2023). Urban exodus? understanding human mobility in britain during the covid-19 pandemic using meta-facebook data. Population, Space and Place, 29(1):e2637.

Santana, C., Botta, F., Barbosa, H., Privitera, F., Menezes, R., and Di Clemente, R. (2023). Covid-19 is linked to changes in the time–space dimension of human mobility. Nature Human Behaviour, 7(10):1729–1739.

Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., and González, M. C. (2013). Unravelling daily human mobility motifs. Journal of The Royal Society Interface, 10(84):20130246.

Song, C., Koren, T., Wang, P., and Barabási, A.-L. (2010). Modelling the scaling properties of human mobility. Nature physics, 6(10):818–823.

Xiong, Q., Liu, Y., Xie, P., Wang, Y., and Liu, Y. (2021). Revealing correlation patterns of individual location activity motifs between workdays and day-offs using massive mobile phone data. Computers, Environment and Urban Systems, 89:101682.

Yang, Y., Pentland, A., and Moro, E. (2023). Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics. EPJ Data Science, 12(1):1–15.

Yao, W., Yu, J., Yang, Y., Chen, N., Jin, S., Hu, Y., and Bai, C. (2022). Understanding travel behavior adjustment under covid-19. Communications in Transportation Research, 2:100068.