UserPoint-MAG: Abordagem de rede multicamada para o estudo de propagação de contágio no transporte público
Resumo
Em tempos de pandemia, o transporte público pode ser crucial para a disseminação de vírus, principalmente nas grandes cidades. As vacinas costumam fazer parte das estratégias para reduzir o contágio; no entanto, estas podem ser escassas em cenários pandêmicos. Utilizando dados do sistema de transporte público, este trabalho propõe o uso de redes multicamadas variantes no tempo para identificar os principais locais críticos a serem considerados prioritários em intervenções, como campanhas de vacinação, para ajudar a reduzir o contágio nesse meio de locomoção. Nossa abordagem considera os pontos de ônibus críticos como pontos prioritários de vacinação, indicando que a vacinação nesses locais reduz a propagação da infecção usando menos doses do que uma vacinação aleatória. A abordagem proposta neste estudo não se limita às estratégias de vacinação, sendo também aplicável a outros problemas que compartilham propriedades semelhantes, mesmo em contextos diferentes.Referências
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Belyi, A., Bojic, I., Sobolevsky, S., Sitko, I., Hawelka, B., Rudikova, L., Kurbatski, A., International and Ratti, C. (2017). Global multi-layer network of human mobility. Journal of Geographical Information Science, 31(7):1381–1402. PMID: 28553155.
Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E., and Havlin, S. (2010). Catastrophic cascade of failures in interdependent networks. Nature, 464(7291):1025–1028.
Cardillo, A., Zanin, M., Gómez-Gardeñes, J., Romance, M., del Amo, A. J. G., and Boccaletti, S. (2013). Modeling the multi-layer nature of the european air transport network: Resilience and passengers re-scheduling under random failures. The European Physical Journal Special Topics.
Chang, S., Pierson, E., Koh, P. W., Gerardin, J., Redbird, B., Grusky, D., and Leskovec, J. (2021). Mobility network models of covid-19 explain inequities and inform reopening. Nature, 589(7840):82–87.
Chodrow, P. S., al Awwad, Z., Jiang, S., and González, M. C. (2016). Demand and congestion in multiplex transportation networks. PLOS ONE, 11(9):1–10.
da Costa, B. M., Bechara, J. V., Wehmuth, K., and Ziviani, A. (2018). A multilayer and time-varying structural analysis of the brazilian air transportation network. In LADaS@VLDB, pages 57–64.
De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Gómez, S., and Arenas, A. (2013). Mathematical formulation of multilayer networks. Phys. Rev. X, 3:041022.
De Domenico, M., Solé-Ribalta, A., Omodei, E., Gómez, S., and Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6(1):6868.
Estrada, E. (2012). The structure of complex networks: theory and applications. Oxford University Press.
Goscé, L. and Johansson, A. (2018). Analysing the link between public transport use and airborne transmission: mobility and contagion in the london underground. Environmental Health, 17(1):84.
Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. SIGMOD Rec., 14(2):47–57.
Hristova, D., Williams, M. J., Musolesi, M., Panzarasa, P., and Mascolo, C. (2016). Measuring urban social diversity using interconnected geo-social networks. In Proceedings of the 25th International Conference on World Wide Web, WWW ’16, page 21–30, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee.
Huang, L., Yang, Y., Gao, H., Zhao, X., and Du, Z. (2018). Comparing community detection algorithms in transport networks via points of interest. IEEE Access, 6:29729– 29738.
Instituto Brasileiro de Geografia e Estatística IBGE (2010). Censo 2010. https://censo2010.ibge.gov.br. [Online; acessado em 07-Novembro-2019].
Jacobsen, K. A., Burch, M. G., Tien, J. H., and Rempaa, G. A. (2018). The large graph limit of a stochastic epidemic model on a dynamic multilayer network. Journal of Biological Dynamics, 12(1):746–788. PMID: 30175687.
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., and Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3):203–271.
Kurant, M. and Thiran, P. (2006). Layered complex networks. Physical Review Letters, 96(13).
Liu, Y. and Seah, H. S. (2015). Points of interest recommendation from gps trajectories. International Journal of Geographical Information Science, 29(6):953–979.
Lv, Q., Qiao, Y., Zhang, Y., Abdesslem, F. B., Lin, W., and Yang, J. (2018). Measuring geospatial properties: Relating online content browsing behaviors to users’ points of interest. Wireless Personal Communications, 101(3):1469–1498.
Meyers, L. A., Newman, M., and Pourbohloul, B. (2006). Predicting epidemics on directed contact networks. Journal of Theoretical Biology, 240(3):400–418.
Mo, B., Feng, K., Shen, Y., Tam, C., Li, D., Yin, Y., and Zhao, J. (2021). Modeling epidemic spreading through public transit using time-varying encounter network. Transportation Research Part C: Emerging Technologies, 122:102893.
Müller, S. A., Balmer, M., Charlton, B., Ewert, R., Neumann, A., Rakow, C., Schlenther, T., and Nagel, K. (2020). Using mobile phone data for epidemiological simulations of lockdowns: government interventions, behavioral changes, and resulting changes of reinfections. medRxiv.
Newman, M. (2010). Networks: An Introduction. Oxford University Press.
Oselio, B., Kulesza, A., and Hero, A. O. (2014). Multi-layer graph analysis for dynamic social networks. IEEE Journal of Selected Topics in Signal Processing, 8(4):514–523.
Piraveenan, M., Prokopenko, M., and Hossain, L. (2013). Percolation centrality: Quantifying graph-theoretic impact of nodes during percolation in networks. PLOS ONE, 8(1):1–14.
Rodrigues, D., Boukerch, A., Silva, T. H., Loureiro, A., and Villas, L. (2017). Smaframework: Urban data integration framework for mobility analysis in smart cities. In Proc. of the 20th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Miami, USA.
Sahneh, F. D., Vajdi, A., Melander, J., and Scoglio, C. M. (2019). Contact adaption during epidemics: A multilayer network formulation approach. IEEE Transactions on Network Science and Engineering, 6(1):16–30.
Sander, L., Warren, C., Sokolov, I., Simon, C., and Koopman, J. (2002). Percolation on heterogeneous networks as a model for epidemics. Mathematical Biosciences, 180(1):293–305.
Santin, P., Gubert, F. R., Fonseca, M., Munaretto, A., and Silva, T. H. (2020). Characterization of public transit mobility patterns of different economic classes. Sustainability, 12(22):9603.
Sun, L., Axhausen, K. W., Lee, D.-H., and Cebrian, M. (2014). Efficient detection of contagious outbreaks in massive metropolitan encounter networks. Scientific Reports, 4(1):5099.
Tang, W., Chakeri, A., and Krim, H. (2020). Discovering urban functional zones by latent fusion of users gps data and points of interests.
Taniguchi, G. and Duarte, F. (2012). Personal smart cards: From transportation to a city smart card—the database integration of public services in Curitiba. In City Competitiveness and Improving Urban Subsystems: Technologies and Applications, pages 217–232. IGI Global.
Thomee, B., Arapakis, I., and Shamma, D. A. (2016). Finding social points of interest from georeferenced and oriented online photographs. ACM Trans. Multimedia Comput. Commun. Appl., 12(2).
Urbanização de Curitiba S/A URBS (2018). URBS em números. https://www.urbs.curitiba.pr.gov.br/institucional/urbs-em-numeros. [Online; acessado em 07-Novembro-2019].
Ventresca, M. and Aleman, D. (2013). Evaluation of strategies to mitigate contagion spread using social network characteristics. Social Networks, 35(1):75–88.
Wang, Y., Liang, Y., Sun, H., and Yang, Y. (2020). Emergency response for covid-19 prevention and control in urban rail transit based on case-based reasoning method. Discrete Dynamics in Nature and Society, 2020:6689089.
Wehmuth, K., Fleury, E., and Ziviani, A. (2016). On multiaspect graphs. Theoretical Computer Science, 651:50–61.
World Health Organization (2022). Who coronavirus disease (covid-19) dashboard. https://covid19.who.int/table. [Online; accessed in 31-January-2022].
Yildirimoglu, M. and Kim, J. (2018). Identification of communities in urban mobility networks using multi-layer graphs of network traffic. Transportation Research Part C: Emerging Technologies, 89:254–267.
Zheng, M., Wang, W., Tang, M., Zhou, J., Boccaletti, S., and Liu, Z. (2018). Multiple peaks patterns of epidemic spreading in multi-layer networks. Chaos, Solitons Fractals, 107:135–142.
Zhu, S., Srebric, J., Spengler, J. D., and Demokritou, P. (2012). An advanced numerical model for the assessment of airborne transmission of influenza in bus microenvironments. Building and Environment, 47:67 – 75. International Workshop on Ventilation, Comfort, and Health in Transport Vehicles.
Publicado
23/05/2022
Como Citar
SANTIN, Priscila; GUBERT, Fernanda R.; FONSECA, Mauro; MUNARETTO, Anelise; SILVA, Thiago H..
UserPoint-MAG: Abordagem de rede multicamada para o estudo de propagação de contágio no transporte público. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 40. , 2022, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 168-181.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2022.221981.
