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Article

Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence

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Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia
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Centre for Lifelong Learning, Universiti Brunei Darussalam, Bandar Seri Begawan BE1410, Brunei
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Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 44000, Pakistan
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Maroof International Hospital, Islamabad 44000, Pakistan
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Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
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Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
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Hamdard Institute of Pharmaceutical Sciences, Hamdard University, Islamabad 44000, Pakistan
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TMR Consulting, Microsoft Gold Partners, Islamabad 44000, Pakistan
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School of Science and Technology (SST), Bangladesh Open University (BOU), Gazipur 1705, Bangladesh
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School of Information Management and Engineering, Zhejiang University of Finance and Economics, Hangzhou 310018, China
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Authors to whom correspondence should be addressed.
Academic Editors: Marc A. Rosen and Amir Mosavi
Sustainability 2021, 13(20), 11339; https://doi.org/10.3390/su132011339
Received: 6 July 2021 / Revised: 28 September 2021 / Accepted: 1 October 2021 / Published: 14 October 2021
(This article belongs to the Special Issue Sustainable Development of Social Commerce in the New Era)
Forecasting disease outbreaks in real-time using time-series data can help for the planning of public health interventions. We used a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), World Health Organization (WHO), and the Centers for Disease Control and Prevention (CDC) to predict upcoming records before the WHO made an official declaration. Our study, conducted on the time series data available from 22 January till 10 March 2020, revealed that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. The initial insight that confirmed COVID-19 cases were increasing was because these received the highest number of effects for our selected dataset from 22 January to 10 March 2020, i.e., 126,344 (64%). The recovered cases were 68289 (34%), and the death rate was around 2%. Moreover, we classified the tweets from 22 January to 15 April 2020 into positive and negative sentiments to identify the emotions (stress or relaxed) posted by Twitter users related to the COVID-19 pandemic. Our analysis identified that tweets mostly conveyed a negative sentiment with a high frequency of words for #coronavirus and #lockdown amid COVID-19. However, these anxiety tweets are an alarm for healthcare authorities to devise plans accordingly. View Full-Text
Keywords: COVID-19; exploratory data analysis; predictive analysis; pandemic; quarantine; anxiety and stress COVID-19; exploratory data analysis; predictive analysis; pandemic; quarantine; anxiety and stress
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MDPI and ACS Style

Alghazzawi, D.; Qazi, A.; Qazi, J.; Naseer, K.; Zeeshan, M.; Abo, M.E.M.; Hasan, N.; Qazi, S.; Naz, K.; Dey, S.K.; Yang, S. Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence. Sustainability 2021, 13, 11339. https://doi.org/10.3390/su132011339

AMA Style

Alghazzawi D, Qazi A, Qazi J, Naseer K, Zeeshan M, Abo MEM, Hasan N, Qazi S, Naz K, Dey SK, Yang S. Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence. Sustainability. 2021; 13(20):11339. https://doi.org/10.3390/su132011339

Chicago/Turabian Style

Alghazzawi, Daniyal, Atika Qazi, Javaria Qazi, Khulla Naseer, Muhammad Zeeshan, Mohamed E.M. Abo, Najmul Hasan, Shiza Qazi, Kiran Naz, Samrat K. Dey, and Shuiqing Yang. 2021. "Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence" Sustainability 13, no. 20: 11339. https://doi.org/10.3390/su132011339

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