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

Daniyal Alghazzawi, Atika Qazi, Javaria Qazi, Khulla Naseer, Muhammad Zeeshan, Mohamed Elhag Mohamed Abo, Najmul Hasan, Shiza Qazi, Kiran Naz, Samrat Kumar Dey and Shuiqing Yang
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Daniyal Alghazzawi: Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia
Atika Qazi: Centre for Lifelong Learning, Universiti Brunei Darussalam, Bandar Seri Begawan BE1410, Brunei
Javaria Qazi: Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 44000, Pakistan
Khulla Naseer: Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 44000, Pakistan
Muhammad Zeeshan: Maroof International Hospital, Islamabad 44000, Pakistan
Mohamed Elhag Mohamed Abo: Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Najmul Hasan: Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
Shiza Qazi: Hamdard Institute of Pharmaceutical Sciences, Hamdard University, Islamabad 44000, Pakistan
Kiran Naz: TMR Consulting, Microsoft Gold Partners, Islamabad 44000, Pakistan
Samrat Kumar Dey: School of Science and Technology (SST), Bangladesh Open University (BOU), Gazipur 1705, Bangladesh
Shuiqing Yang: School of Information Management and Engineering, Zhejiang University of Finance and Economics, Hangzhou 310018, China

Sustainability, 2021, vol. 13, issue 20, 1-16

Abstract: 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.

Keywords: COVID-19; exploratory data analysis; predictive analysis; pandemic; quarantine; anxiety and stress (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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