Applying Graph Databases and Human Mobility Data to Track Infectious Disease Spread in Brazil
DOI:
https://doi.org/10.5753/jidm.2025.4291Keywords:
Graph Database, Human Mobility, Open Data, Infectious Disease, Neo4jAbstract
This work has been enriched by the invaluable contributions of the following institutions: The ÆSOP (Alert-Early System of Outbreaks with Pandemic Potential) project provided us with the primary idea and guidance for this research, laying the foundation for our study. CIn-UFPE, SiDi, and Samsung Brazil, who supported the ``Data Engineering and Data Science'' Residency program, where this study was successfully applied as part of our coursework, culminating in its completion. The UFRPE collaborative efforts and orientation support played a pivotal role in the successful execution and completion of this research project.
Downloads
References
Bajardi, P., Poletto, C., Ramasco, J. J., Tizzoni, M., Colizza, V., and Vespignani, A. (2011). Human mobility networks, travel restrictions, and the global spread of 2009 h1n1 pandemic. PloS one, 6(1):e16591.
Balcan, D., Gonçalves, B., Hu, H., Ramasco, J. J., Colizza, V., and Vespignani, A. (2010). Modeling the spatial spread of infectious diseases: The global epidemic and mobility computational model. Journal of computational science, 1(3):132–145.
Brockmann, D. and Helbing, D. (2013). The hidden geometry of complex, network-driven contagion phenomena. science, 342(6164):1337–1342.
Dash (2022). Dash python user guide. ”url=[link]”. Retrieved November 14, 2022.
Dijkstra, E. W. (2022). A note on two problems in connexion with graphs. In Edsger Wybe Dijkstra: His Life, Work, and Legacy, pages 287–290.
EpiRisk (2022). Epirisk. ”url=[link]”. Retrieved November 14, 2022.
Faria, N. R., Mellan, T. A., Whittaker, C., Claro, I. M., Candido, D. d. S., Mishra, S., Crispim, M. A., Sales, F. C., Hawryluk, I., McCrone, J. T., et al. (2021). Genomics and epidemiology of the p. 1 sars-cov-2 lineage in manaus, brazil. Science, 372(6544):815–821.
Gilbert, M., Pullano, G., Pinotti, F., Valdano, E., Poletto, C., Boëlle, P.-Y., d’Ortenzio, E., Yazdanpanah, Y., Eholie, S. P., Altmann, M., et al. (2020). Preparedness and vulnerability of african countries against importations of covid-19: a modelling study. The Lancet, 395(10227):871–877.
Keeling, M. J. and Rohani, P. (2008). Modeling Infectious Diseases in Human and Animals. Princeton University Press.
Ministério da Infraestrutura (2020). Anuário estatístico de transportes 2010 - 2020. ”url=[link]”. Retrieved November 14, 2022.
Mu, X., Yeh, A. G.-O., and Zhang, X. (2021). The interplay of spatial spread of covid-19 and human mobility in the urban system of china during the chinese new year. Environment and Planning B: Urban Analytics and City Science, 48(7):1955–1971.
Nakamura, H. and Managi, S. (2020). Airport risk of importation and exportation of the covid-19 pandemic. Transport policy, 96:40–47.
Neo4j (2022). Neo4j. ”url=[link]”. Retrieved November 14, 2022.
Neo4j GDS (2022). The neo4j graph data science library manual v2.2. ”url=[
Oliveira, M., Alencar, A., Oliveira, N., Sales, L., Cunha, A., and Ramos, P. (2023). Epiflow: a hybrid approach to track infectious disease spread in brazil based on travel data and graph databases. In Proceedings of the 38th Brazilian Symposium on Databases, pages 218–230, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbbd.2023.231736.
Orrico Filho, R. D., DNIT, and COPPE/UFRJ (2019). Dados de origem e destino das pesquisas nacionais de tráfego de 2016 e 2017. Nota Técnica 04/2019/DE, Departamento Nacional de Infraestrutura de Transportes (DNIT), Rio de Janeiro. Termo de Execução Descentralizada - TED 964/2014 — DPP.
Peixoto, P. S., Marcondes, D., Peixoto, C., and Oliva, S. M. (2020). Modeling future spread of infections via mobile geolocation data and population dynamics. an application to covid-19 in brazil. PloS one, 15(7):e0235732.
PostgreSQL (2022). Postgresql. ”url=[link]”. Retrieved November 14, 2022.
Reback, J., McKinney, W., Van Den Bossche, J., Augspurger, T., Cloud, P., Klein, A., Hawkins, S., Roeschke, M., Tratner, J., She, C., et al. (2020). pandas-dev/pandas: Pandas 1.0. 5. Zenodo.
Sadekar, O., Budamagunta, M., Sreejith, G., Jain, S., and Santhanam, M. (2021). An infectious diseases hazard map for india based on mobility and transportation networks. arXiv preprint arXiv:2105.15123.
Statista (2022). Number of flights performed by the global airline industry from 2004 to 2022.
”url=[link]”. Retrieved November 14, 2022.
Wei, Y., Wang, J., Song, W., Xiu, C., Ma, L., and Pei, T. (2021). Spread of covid-19 in china: analysis from a city-based epidemic and mobility model. Cities, 110:103010.
WHO (2022a). Who coronavirus (covid-19) dashboard. ”url=[link]”. Retrieved November 14, 2022.
WHO (2022b). Who releases 10-year strategy for genomic surveillance of pathogens. ”url=[link]”. Retrieved November 14, 2022.
