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Analysis of data sets that may be changing often or in real-time, consists of at least three important synchronized components: i) figuring out what to infer (objectives), ii) analysis or computation of those objectives, and iii) understanding of the results which may require drill-down and/or visual-ization. There is considerable research on the first two of the above compo-nents whereas understanding actionable inferences through visualization has not been addressed properly. Visualization is an important step towards both understanding (especially by non-experts) and inferring the actions that need to be taken. As an example, for Covid-19, knowing regions (say, at the county or state level) that have seen a spike or are prone to a spike in the near future may warrant additional actions with respect to gatherings, business opening hours, etc. This paper focuses on a modular and extensible architecture for visualization of base as well as analyzed data. This paper proposes a modular architecture of a dashboard for user inter-action, visualization management, and support for complex analysis of base data. The contributions of this paper are: i) extensibility of the architecture providing flexibility to add additional analysis, visualizations, and user interac-tions without changing the workflow, ii) decoupling of the functional modules to ease and speed up development by different groups, and iii) supporting con-current users and addressing efficiency issues for display response time. This paper uses Multilayer Networks (or MLNs) for analysis. To showcase the above, we present the architecture of a visualization dash-board, termed CoWiz++ (for Covid Wizard), and elaborate on how web-based user interaction and display components are interfaced seamlessly with the back-end modules. 

A visualization dashboard for Covid 19 understanding using multilayered network analysis underneath 

Algorithm for converting Enhanced Entity Relationship diagrams into Multilayer Networks for analysis and Relational schema for drill down. 

Making a case for multilayer Networks for modeling and analysis of data with multiple entity and relationship types. 

Algorithms for degree centrality computation on homogeneous MLNs using the decoupling-based approach.