Abstract
At the end of December 2019, the Wuhan Municipal Health Commission, revealed several cases of pneumonia of unknown etiology. Later, this etiology was called the coronavirus disease 2019 (COVID-19). COVID-19 disease is rapidly spreading around the globe, affected millions of people, compelling governments to take serious actions. Due to this deadly disease, a number of deaths have been occurred and still increasing exponentially. In the practice and application of big data sciences, it is always of interest to provide the best description of the data. In this present article, the event background, symptoms, and preventions from COVID-19 are discussed. The steps were taken by the Chinese government to control the COVID-19 has also been discussed. Up to date, details, and data of daily discovered cases, total discovered cases, daily deaths, and total deaths around the world are presented. Moreover, a new statistical distribution is introduced to provide the best characterization of the survival times of the patients affected by the COVID-19 in China. By analyzing the survival times of the COVID-19 patient's data, it is showed that the new model provides a closer fit to COVID-19 events. (c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).