Open Access

ARTICLE

Pandemic Analysis and Prediction of COVID-19 Using Gaussian Doubling Times

Saleh Albahli^1,*, Farman Hassan², Ali Javed^2,3, Aun Irtaza^2,4
1 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
2 Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
3 Department of Computer Science and Engineering, Oakland University, MI, USA
4 Department of Computer and Electrical Engineering, University of Michigan, Dearborn, USA
* Corresponding Author: Saleh Albahli. Email:
(This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2022, 72(1), 833-849. https://doi.org/10.32604/cmc.2022.024267

Received 11 October 2021; Accepted 24 December 2021; Issue published 24 February 2022

Abstract

COVID-19 has become a pandemic, with cases all over the world, with widespread disruption in some countries, such as Italy, US, India, South Korea, and Japan. Early and reliable detection of COVID-19 is mandatory to control the spread of infection. Moreover, prediction of COVID-19 spread in near future is also crucial to better plan for the disease control. For this purpose, we proposed a robust framework for the analysis, prediction, and detection of COVID-19. We make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the world. The estimates, analysis and predictions are based on the data gathered from Johns Hopkins Center during the time span of April 21 to June 27, 2020. We use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different continents. The predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well justified. The parameters of Gaussian distribution, i.e., maximum time and width, are determined through a statistical χ2-fit for the purpose of doubling times after April 21, 2020. For COVID-19 detection, we proposed a novel method based on the Histogram of Oriented Gradients (HOG) and CNN in multi-class classification scenario i.e., Normal, COVID-19, viral pneumonia etc. Experimental results show the effectiveness of our framework for reliable prediction and detection of COVID-19.

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Keywords

Prediction; coronavirus; time-series prediction; gaussian; mathematical model; pandemic spreading

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