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Continent-Wide Analysis of COVID 19: Total Cases, Deaths, Tests, Socio-Economic, and Morbidity Factors Associated to the Mortality Rate, and Forecasting Analysis in 2020–2021

Author

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  • Muhammad Nauman Zahid

    (Department of Biology, College of Science, University of Bahrain, Sakhir 32038, Bahrain
    The two authors contributed equally to this work.)

  • Simone Perna

    (Department of Biology, College of Science, University of Bahrain, Sakhir 32038, Bahrain
    The two authors contributed equally to this work.)

Abstract

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in China in December 2019 and has become a pandemic that resulted in more than one million deaths and infected over 35 million people worldwide. In this study, a continent-wide analysis of COVID-19 cases from 31st December 2019 to 14th June 2020 was performed along with socio-economic factors associated with mortality rates as well as a predicted future scenario of COVID-19 cases until the end of 2020. Methods: Epidemiological and statistical tools such as linear regression, Pearson’s correlation analysis, and the Auto Regressive Integrated Moving Average (ARIMA) model were used in this study. Results: This study shows that the highest number of cases per million population was recorded in Europe, while the trend of new cases is lowest in Africa. The mortality rates in different continents were as follows: North America 4.57%, Europe 3.74%, South America 3.87%, Africa 3.49%, Oceania and Asia less than 2%. Linear regression analysis showed that hospital beds, GDP, diabetes, and higher average age were the significant risk factors for mortality in different continents. The forecasting analysis since the first case of COVID-19 until 1st January 2021 showed that the worst scenario at the end of 2020 predicts a range from 0 to 300,000 daily new cases and a range from 0 to 16,000 daily new deaths. Conclusion: Epidemiological and clinical features of COVID-19 should be better defined, since they can play an import role in future strategies to control this pandemic.

Suggested Citation

  • Muhammad Nauman Zahid & Simone Perna, 2021. "Continent-Wide Analysis of COVID 19: Total Cases, Deaths, Tests, Socio-Economic, and Morbidity Factors Associated to the Mortality Rate, and Forecasting Analysis in 2020–2021," IJERPH, MDPI, vol. 18(10), pages 1-10, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5350-:d:556529
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    References listed on IDEAS

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    1. Edith Lahner & Emanuele Dilaghi & Claudio Prestigiacomo & Giuliano Alessio & Laura Marcellini & Maurizio Simmaco & Iolanda Santino & Giovanni Battista Orsi & Paolo Anibaldi & Adriano Marcolongo & Brun, 2020. "Prevalence of Sars-Cov-2 Infection in Health Workers (HWs) and Diagnostic Test Performance: The Experience of a Teaching Hospital in Central Italy," IJERPH, MDPI, vol. 17(12), pages 1-12, June.
    2. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
    3. Hari Vishal Lakhani & Sneha S. Pillai & Mishghan Zehra & Ishita Sharma & Komal Sodhi, 2020. "Systematic Review of Clinical Insights into Novel Coronavirus (CoVID-19) Pandemic: Persisting Challenges in U.S. Rural Population," IJERPH, MDPI, vol. 17(12), pages 1-14, June.
    4. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H, 2020. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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    1. Małgorzata Dudzińska & Marta Gwiaździńska-Goraj & Aleksandra Jezierska-Thöle, 2022. "Social Factors as Major Determinants of Rural Development Variation for Predicting Epidemic Vulnerability: A Lesson for the Future," IJERPH, MDPI, vol. 19(21), pages 1-24, October.
    2. Devi Prasad Dash & Narayan Sethi, 2022. "Pandemics, Lockdown And Economic Growth: A Region-Specific Perspective On Covid-19," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 25(Special I), pages 43-60, March.
    3. Jean-Claude Kouladoum, 2023. "Inclusive Education and Health Performance in Sub Saharan Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(3), pages 879-900, February.

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