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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Daniyal Alghazzawi & Atika Qazi & Javaria Qazi & Khulla Naseer & Muhammad Zeeshan & Mohamed Elhag Mohamed Abo & Najmul Hasan & Shiza Qazi & Kiran Naz & Samrat Kumar Dey & Shuiqing Yang, 2021. "Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence," Sustainability, MDPI, vol. 13(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11339-:d:655795
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    References listed on IDEAS

    as
    1. Qazi, Atika & Qazi, Javaria & Naseer, Khulla & Zeeshan, Muhammad & Qazi, Shiza & Abayomi-Alli, Olusola & Said Ahmad, Ibrahim & Darwich, Mohammad & Ali Talpur, Bandeh & Hardaker, Glenn & Naseem, Usman , 2021. "Adaption of distance learning to continue the academic year amid COVID-19 lockdown," Children and Youth Services Review, Elsevier, vol. 126(C).
    2. Mohamed Elhag Mohamed Abo & Norisma Idris & Rohana Mahmud & Atika Qazi & Ibrahim Abaker Targio Hashem & Jaafar Zubairu Maitama & Usman Naseem & Shah Khalid Khan & Shuiqing Yang, 2021. "A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    3. Sara Bentivegna & Giovanni Boccia Artieri, 2020. "Rethinking Public Agenda in a Time of High-Choice Media Environment," Media and Communication, Cogitatio Press, vol. 8(4), pages 6-15.
    4. Breland, J.Y. & Quintiliani, L.M. & Schneider, K.L. & May, C.N. & Pagoto, S., 2017. "Social Media as a Tool to Increase the Impact of Public Health Research," American Journal of Public Health, American Public Health Association, vol. 107(12), pages 1890-1891.
    5. Hafiz Suliman Munawar & Sara Imran Khan & Zakria Qadir & Abbas Z. Kouzani & M A Parvez Mahmud, 2021. "Insight into the Impact of COVID-19 on Australian Transportation Sector: An Economic and Community-Based Perspective," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    6. Qazi, Atika & Naseer, Khulla & Qazi, Javaria & AlSalman, Hussain & Naseem, Usman & Yang, Shuiqing & Hardaker, Glenn & Gumaei, Abdu, 2020. "Conventional to online education during COVID-19 pandemic: Do develop and underdeveloped nations cope alike," Children and Youth Services Review, Elsevier, vol. 119(C).
    7. De Rosis, Sabina & Lopreite, Milena & Puliga, Michelangelo & Vainieri, Milena, 2021. "The early weeks of the Italian Covid-19 outbreak: sentiment insights from a Twitter analysis," Health Policy, Elsevier, vol. 125(8), pages 987-994.
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