The R Shiny framework serves as a platform for visualization and analysis of the data, as well as an advance to capitalize on existing data curation to support and enable open science. The coded analysis here includes logistic and Gompertz growth models, as two mathematical tools for forecasting the future of the COVID-19 pandemic, as well as the Moran's index metric, which gives a spatial perspective via heat maps that may assist in the identification of latent responses and behavioral patterns. This analysis provides real-time statistical application aiming to make sense to academic- and public consumers of the large amount of data that is being accumulated due to the COVID-19 pandemic. In order to see more details on the dashboard, see Salehi et al. (2020).

Reference:

[1] Salehi M, Arashi M, Bekker A, Ferreira J, Chen D-G, Esmaeili F, and Frances M (2020). A Synergetic R-Shiny Portal for Modeling and Tracking of COVID-19 Data. Front. Public Health 8:623624.doi: 10.3389/fpubh.2020.623624.