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. 2022 Mar;39(3):e12714.
doi: 10.1111/exsy.12714. Epub 2021 May 11.

Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques

Dimple Tiwari  1 , Bhoopesh Singh Bhati  1 , Fadi Al-Turjman  2 , Bharti Nagpal  1
Affiliations

Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques

Dimple Tiwari et al. Expert Syst. 2022 Mar.

Abstract

Pandemic novel Coronavirus (Covid-19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid-19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid-19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid-19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)-based meta-analysis to predict the trend of epidemic Covid-19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time-series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid-19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid-19 observed symptoms, a list of Top-20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid-19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction.

Keywords: Covid‐19; Naïve Bayes; artificial intelligence; data analytics; linear regression; machine‐learning prediction; support vector machine.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Covid‐19 symptoms (systemic disorders VS respiratory disorders)
FIGURE 2
FIGURE 2
Percentage chart of Covid‐19 symptoms
FIGURE 3
FIGURE 3
Word cloud of Covid‐19 common symptoms
FIGURE 4
FIGURE 4
Active cases of Covid‐19 in the world
FIGURE 5
FIGURE 5
Covid‐19 cases in the entire world (a) Represents Coronavirus confirmed cases in the world (b) Represents Coronavirus recovered cases in the world (c) Represents Coronavirus death cases in the world (d) Represents Coronavirus active cases in the world
FIGURE 6
FIGURE 6
Daily increase in Covid‐19 pandemic cases in the world (a) The daily increase in confirmed cases (b) The daily increase in recovered cases in the world (c) The daily increase in death cases in the world
FIGURE 7
FIGURE 7
Top‐5 countries (US, Russia, Brazil, UK, Spain) Covid‐19 cases (a) The number of confirmed Covid‐19 cases in top‐5 countries (b) The number of recovered Covid‐19 cases in top‐5 countries (c) The number of deaths Covid‐19 cases in top‐5 countries
FIGURE 8
FIGURE 8
The procedure of Covid‐19 analytical study using Machine‐Learning techniques
FIGURE 9
FIGURE 9
The test confirmed cases vs Bayesian prediction of Covid‐19 in the world
FIGURE 10
FIGURE 10
Total confirmed cases vs Bayesian predictions for Covid‐19 in the world
FIGURE 11
FIGURE 11
The test confirmed cases Vs SVM prediction of Covid‐19 in the world
FIGURE 12
FIGURE 12
Total confirmed cases Vs SVM predictions for Covid‐19 in the world
FIGURE 13
FIGURE 13
The test confirmed cases Vs Regression prediction of Covid‐19 in the world
FIGURE 14
FIGURE 14
Total confirmed cases Vs Regression predictions for Covid‐19 in the world
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