Malar J. 2022 Aug 1;21(1):232. doi: 10.1186/s12936-022-04248-w.
BACKGROUND: Data integration and visualisation techniques have been widely used in scientific research to allow the exploitation of large volumes of data and support highly complex or long-lasting research questions. Integration allows data from different sources to be aggregated into a single database comprising variables of interest for different types of studies. Visualisation allows large and complex data sets to be manipulated and interpreted in a more intuitive way.
METHODS: Integration and visualisation techniques were applied in a malaria surveillance ecosystem to build an integrated database comprising notifications, deaths, vector control and climate data. This database is accessed through Malaria-VisAnalytics, a visual mining platform for descriptive and predictive analysis supporting decision and policy-making by governmental and health agents.
RESULTS: Experimental and validation results have proved that the visual exploration and interaction mechanisms allow effective surveillance for rapid action in suspected outbreaks, as well as support a set of different research questions over integrated malaria electronic health records.
CONCLUSION: The integrated database and the visual mining platform (Malaria-VisAnalytics) allow different types of users to explore malaria-related data in a user-friendly interface. Summary data and key insights can be obtained through different techniques and dimensions. The case study on Manaus can serve as a reference for future replication in other municipalities. Finally, both the database and the visual mining platform can be extended with new data sources and functionalities to accommodate more complex scenarios (such as real-time data capture and analysis).