Article
Version 1
Preserved in Portico This version is not peer-reviewed
COVID-19 Real-Time Tracker and Analytical Report
Version 1
: Received: 4 June 2020 / Approved: 7 June 2020 / Online: 7 June 2020 (07:44:48 CEST)
How to cite: Long, J. COVID-19 Real-Time Tracker and Analytical Report. Preprints 2020, 2020060063. https://doi.org/10.20944/preprints202006.0063.v1 Long, J. COVID-19 Real-Time Tracker and Analytical Report. Preprints 2020, 2020060063. https://doi.org/10.20944/preprints202006.0063.v1
Abstract
While the COVID-19 outbreak was reported to first originate from Wuhan, China, it has been declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by WHO, and it has spread to over 180 countries by the time of this paper was being composed. As the disease spreads around the globe, it has evolved into a worldwide pandemic, endangering the state of global public health and becoming a serious threat to the global community. To combat and prevent the spread of the disease, all individuals should be well-informed of the rapidly changing state of COVID-19. In the endeavor of accomplishing this objective, a COVID-19 real-time analytical tracker has been built to provide the latest status of the disease and relevant analytical insights. The real-time tracker is designed to cater to the general audience without advanced statistical aptitude. It aims to communicate insights through various straightforward and concise data visualizations that are supported by sound statistical foundations and reliable data sources. This paper aims to discuss the major methodologies which are utilized to generate the insights displayed on the real-time tracker, which include real-time data retrieval, normalization techniques, ARIMA time-series forecasting, and logistic regression models. In addition to introducing the details and motivations of the utilized methodologies, the paper additionally features some key discoveries that have been derived in regard to COVID-19 using the methodologies.
Supplementary and Associated Material
https://peterljw.shinyapps.io/covid_dashboard/: A COVID-19 real-time tracker that provides the latest status of the disease and relevant analytical insights.
Keywords
COVID-19; Real-Time Tracker; Common Symptoms; Data Visualization; Hypothesis Testing; ARIMA Time-Series Forecast; Penalized Logistic Regression
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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