Article
Version 2
Preserved in Portico This version is not peer-reviewed
Optimistic Prediction Model for the COVID-19 Coronavirus Pandemic Based on the Reported Data Analysis
Version 1
: Received: 22 May 2020 / Approved: 24 May 2020 / Online: 24 May 2020 (19:03:18 CEST)
Version 2 : Received: 28 May 2020 / Approved: 28 May 2020 / Online: 28 May 2020 (03:09:38 CEST)
Version 2 : Received: 28 May 2020 / Approved: 28 May 2020 / Online: 28 May 2020 (03:09:38 CEST)
How to cite: Aloufi, K. Optimistic Prediction Model for the COVID-19 Coronavirus Pandemic Based on the Reported Data Analysis. Preprints 2020, 2020050399. https://doi.org/10.20944/preprints202005.0399.v2 Aloufi, K. Optimistic Prediction Model for the COVID-19 Coronavirus Pandemic Based on the Reported Data Analysis. Preprints 2020, 2020050399. https://doi.org/10.20944/preprints202005.0399.v2
Abstract
The world is facing new challenges every day; however, with the spread of the pandemic around the world, this new challenge is different. The pandemic is increasing and concentrating various challenges simultaneously. Although different sectors are facing consequences, the most important sectors, that is, health and economy are the most affected. When the pandemic began, it was not known how long it would last, which complicated health and economic planning. Therefore, it is important for decision makers and the public to know the predictions and expectations of the future of these challenges. In this work, the current situation is analyzed. Then, an expectation model is developed based on the statistics of the pandemic using a growth rate model based on an exponential and logarithmic rate of increase. Based on the available open data about the pandemic spread, the model can successfully predict future expectations, including the duration and maximum number of cases of the pandemic. The model uses the equilibrium point as the day the cases decrease. The model can be used for planning and the development of strategies to overcome these challenges.
Supplementary and Associated Material
http://dx.doi.org/10.17632/zk32frw6p5.6: Covid-19 Dataset
Keywords
IR; ML; Data Analysis; COVID-19; Coronavirus; Pandemic
Subject
Computer Science and Mathematics, Analysis
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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Commenter: Khalid Aloufi
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