Epiflow: a hybrid approach to track infectious disease spread in Brazil based on travel data and graph databases

• Mariama C. S. de Oliveira Universidade Federal de Pernambuco (UFPE)
• Andrêza Leite de Alencar Univerisade Federal Rural de Pernambuco (UFRPE) https://orcid.org/0000-0002-7083-0646
• Natalia Tatiele S. de Oliveira Universidade Federal de Pernambuco (UFPE)
• Lucas Henrique Gonzaga de Sales Universidade Federal Rural de Pernambuco (UFRPE)
• Antônio Ricardo Khouri Cunha Fundação Oswaldo Cruz (Fiocruz)
• Pablo Ivan Pereira Ramos Fundação Oswaldo Cruz (Fiocruz)

DOI: https://doi.org/10.5753/sbbd.2023.231736

Resumo

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.

Palavras-chave: Graph database, Track infectious disease, Travel Data, Dijkstra, COVID-19, Brazil

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