Epiflow: a hybrid approach to track infectious disease spread in Brazil based on travel data and graph databases - Oliveira, Mariama C. S. de; Alencar, Andrêza Leite de; Oliveira, Natalia Tatiele S. de; Sales, Lucas Henrique Gonzaga de; Cunha, Antônio Ricardo Khouri; Ramos, Pablo Ivan Pereira (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2023: Anais do XXXVIII Simpósio Brasileiro de Bancos de Dados, 2023)
Based on open data on cities and transport, the present study proposes an approach that uses travel probabilities and graph-oriented database to identify possible disease propagation routes within the Brazilian territory. Route identification was implemented by adapting the Dijkstra algorithm in the Data Science module of Neo4j. A tool called Epiflow was also developed to allow visual exploration of the proposed approach. Validated by COVID-19 data, the approach successfully predicted routes for large geographical areas of risk, such as states. These findings suggest that transport data and graph databases can be used to create applications that assist decision-making in tracking disease spread in the early stages.