Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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)

How to cite: Aloufi, K. Optimistic Prediction Model for the COVID-19 Coronavirus Pandemic Based on the Reported Data Analysis. Preprints 2020, 2020050399 (doi: 10.20944/preprints202005.0399.v1). Aloufi, K. Optimistic Prediction Model for the COVID-19 Coronavirus Pandemic Based on the Reported Data Analysis. Preprints 2020, 2020050399 (doi: 10.20944/preprints202005.0399.v1).

Abstract

The world is facing new challenges every day. however, with the pandemic spread over the world, a new challenge is different. The pandemic challenge is taking all the challenges to concentrate and increase in one time. Although different sectors are facing consequences, the most important sectors, health and economy are the most affected. When the pandemic start, it is not known when it will last for different health and economic planning. Therefore, it is very important for decision makers and the public to know the prediction and expectations of the future of these challenges to successfully goes over it. 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 the exponential and logarithmic increase rate. Based on the available open data about the pandemic spread, the model successfully can predict the future expectations. The model expects the time and the maximum number of cases of the pandemic. The model uses the equilibrium point as the day the of cases decreases. The model can be used for planning and development of strategies to overcome the challenges.

Supplementary and Associated Material

Subject Areas

IR; ML; Data Analysis; COVID-19; Coronavirus; Pandemic

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