4.7 Article

A Synergetic R-Shiny Portal for Modeling and Tracking of COVID-19 Data

Journal

FRONTIERS IN PUBLIC HEALTH
Volume 8, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fpubh.2020.623624

Keywords

COVID-19; dashboard; gompertz growth model; logistic growth model; moran's index; open science; r; shiny

Funding

  1. South African National Research Foundation [SRUG190308422768, 120839]
  2. SARChI Research Chair in Computational and Methodological Statistics [71199]
  3. South African DST-NRF-MRC SARChI Research Chair in Biostatistics [114613]
  4. STATOMET at the Department of Statistics at the University of Pretoria
  5. UP Postdoctoral fellowship grant
  6. Iran National Science Foundation (INSF) [99014568]

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This paper introduces an online interactive dashboard that visualizes and tracks confirmed cases of COVID-19 in real-time, aiming to provide a user-friendly tracking tool for researchers and the general public with reliable data sources and built in open-source R software.
The purpose of this paper is to introduce a useful online interactive dashboard () 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.

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