IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i19p7016-d419552.html
   My bibliography  Save this article

An Overview of the World Current and Future Assessment of Novel COVID-19 Trajectory, Impact, and Potential Preventive Strategies at Healthcare Settings

Author

Listed:
  • Bader S. Al-Anzi

    (Department of Environmental Technology Management, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait)

  • Mohammad Alenizi

    (General Department of Criminal Evidences Identification, Ministry of Interior, Safat 12003, Kuwait)

  • Jehad Al Dallal

    (Department of Information Sciences, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait)

  • Frage Lhadi Abookleesh

    (Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada)

  • Aman Ullah

    (Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada)

Abstract

This study is an overview of the current and future trajectory, as well as the impact of the novel Coronavirus (COVID-19) in the world and selected countries including the state of Kuwait. The selected countries were divided into two groups: Group A (China, Switzerland, and Ireland) and Group B (USA, Brazil, and India) based on their outbreak containment of this virus. Then, the actual data for each country were fitted to a regression model utilizing the excel solver software to assess the current and future trajectory of novel COVID-19 and its impact. In addition, the data were fitted using the Susceptible–Infected–Recovered (SIR) Model. The Group A trajectory showed an “S” shape trend that suited a logistic function with r 2 > 0.97, which is an indication of the outbreak control. The SIR models for the countries in this group showed that they passed the expected 99% end of pandemic dates. Group B, however, exhibited a continuous increase of the total COVID-19 new cases, that best suited an exponential growth model with r 2 > 0.97, which meant that the outbreak is still uncontrolled. The SIR models for the countries in this group showed that they are still relatively far away from reaching the expected 97% end of pandemic dates. The maximum death percentage varied from 3.3% (India) to 7.2% with USA recording the highest death percentage, which is virtually equal to the maximum death percentage of the world (7.3%). The power of the exponential model determines the severity of the country’s trajectory that ranged from 11 to 19 with the USA and Brazil having the highest values. The maximum impact of this COVID-19 pandemic occurred during the uncontrolled stage (2), which mainly depended on the deceptive stage (1). Further, some novel potential containment strategies are discussed. Results from both models showed that the Group A countries contained the outbreak, whereas the Group B countries still have not reached this stage yet. Early measures and containment strategies are imperative in suppressing the spread of COVID-19.

Suggested Citation

  • Bader S. Al-Anzi & Mohammad Alenizi & Jehad Al Dallal & Frage Lhadi Abookleesh & Aman Ullah, 2020. "An Overview of the World Current and Future Assessment of Novel COVID-19 Trajectory, Impact, and Potential Preventive Strategies at Healthcare Settings," IJERPH, MDPI, vol. 17(19), pages 1-19, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:7016-:d:419552
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/19/7016/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/19/7016/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Mohamed Abd Elaziz, 2020. "Optimized Forecasting Method for Weekly Influenza Confirmed Cases," IJERPH, MDPI, vol. 17(10), pages 1-12, May.
    2. Nan Zhang & Yuguo Li, 2018. "Transmission of Influenza A in a Student Office Based on Realistic Person-to-Person Contact and Surface Touch Behaviour," IJERPH, MDPI, vol. 15(8), pages 1-20, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rastko Jovanović & Miloš Davidović & Ivan Lazović & Maja Jovanović & Milena Jovašević-Stojanović, 2021. "Modelling Voluntary General Population Vaccination Strategies during COVID-19 Outbreak: Influence of Disease Prevalence," IJERPH, MDPI, vol. 18(12), pages 1-18, June.
    2. Diego Galvan & Luciane Effting & Hágata Cremasco & Carlos Adam Conte-Junior, 2020. "Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?," IJERPH, MDPI, vol. 17(23), pages 1-16, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu-Feng Zhao & Ming-Huan Shou & Zheng-Xin Wang, 2020. "Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models," IJERPH, MDPI, vol. 17(12), pages 1-20, June.
    2. Sheikh Safiullah & Asadur Rahman & Shameem Ahmad Lone & S. M. Suhail Hussain & Taha Selim Ustun, 2022. "Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems," Sustainability, MDPI, vol. 14(21), pages 1-27, November.
    3. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    4. Nan Zhang & Boni Su & Pak-To Chan & Te Miao & Peihua Wang & Yuguo Li, 2020. "Infection Spread and High-Resolution Detection of Close Contact Behaviors," IJERPH, MDPI, vol. 17(4), pages 1-18, February.
    5. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    6. Emily Ying Yang Chan & Holly Ching Yu Lam, 2020. "Research Frontiers of Health Emergency and Disaster Risk Management: What Do We Know So Far?," IJERPH, MDPI, vol. 17(5), pages 1-4, March.
    7. Tian-Shyug Lee & I-Fei Chen & Ting-Jen Chang & Chi-Jie Lu, 2020. "Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme," IJERPH, MDPI, vol. 17(13), pages 1-15, July.
    8. Qiang Wang & Min Su & Min Zhang & Rongrong Li, 2021. "Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare," IJERPH, MDPI, vol. 18(11), pages 1-50, June.
    9. Tasneem Kamal Aldeen Muhamed & Mona Yahya Salim Alfefi & Nahla Morad, 2022. "Analysis Impact of Coronavirus in the Kingdom of Saudi Arabia by Using the Artificial Neural Network," Eximia Journal, Plus Communication Consulting SRL, vol. 5(1), pages 146-157, July.
    10. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Laith Abualigah & Mohamed Abd Elaziz, 2020. "Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea," IJERPH, MDPI, vol. 17(10), pages 1-14, May.
    11. Berik Toleubekov & Zhanerke Bolatova & Martin Stafström, 2022. "Assessing Access to WASH in Urban Schools during COVID-19 in Kazakhstan: Case Study of Central Kazakhstan," IJERPH, MDPI, vol. 19(11), pages 1-12, May.
    12. Pengcheng Zhao & Nan Zhang & Yuguo Li, 2020. "A Comparison of Infection Venues of COVID-19 Case Clusters in Northeast China," IJERPH, MDPI, vol. 17(11), pages 1-13, June.
    13. Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo, 2023. "An accurate and fully-automated ensemble model for weekly time series forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 641-658.
    14. Dabiah Alboaneen & Bernardi Pranggono & Dhahi Alshammari & Nourah Alqahtani & Raja Alyaffer, 2020. "Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia," IJERPH, MDPI, vol. 17(12), pages 1-10, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:7016-:d:419552. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.