A Synergetic R-Shiny Portal for Modeling and Tracking of COVID-19 Data.
The purpose of this paper is to introduce a useful online interactive dashboard (https://mahdisalehi.shinyapps.io/Covid19Dashboard/) that visualize and follow confirmed cases of COVID-19 in real-time. The dashboard was made publicly available on 6 April 2020 to illustrate the counts of confirmed cases, deaths, and recoveries of COVID-19 at the level of country or continent. This dashboard is intended as a user-friendly dashboard for researchers as well as the general public to track the COVID-19 pandemic, and is generated from trusted data sources and built in open-source R software (Shiny in particular); ensuring a high sense of transparency and reproducibility. 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. Coded analysis here includes logistic and Gompertz growth models, as two mathematical tools for predicting 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.
['Salehi|Mahdi|M|', 'Arashi|Mohammad|M|', 'Bekker|Andriette|A|', 'Ferreira|Johan|J|', 'Chen|Ding-Geng|DG|', 'Esmaeili|Foad|F|', 'Frances|Motala|M|']
D000086382:COVID-19 D003626:Data Display D066264:Datasets as Topic D006801:Humans D016247:Information Storage and Retrieval D016015:Logistic Models D058873:Pandemics D015203:Reproducibility of Results D014584:User-Computer Interface D064878:Web Browser
COVID-19 dashboard r shiny open science gompertz growth model moran's index logistic growth model