IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v197y2023ics0040162523005711.html
   My bibliography  Save this article

Data-driven decision making for modelling covid-19 and its implications: A cross-country study

Author

Listed:
  • Sariyer, Gorkem
  • Mangla, Sachin Kumar
  • Kazancoglu, Yigit
  • Jain, Vranda
  • Ataman, Mustafa Gokalp

Abstract

Grounded in big data analytics capabilities, this study aims to model the COVID-19 spread globally by considering various factors such as demographic, cultural, health system, economic, technological, and policy-based. Classified values on each country's case, death, and recovery numbers (per 1000,000 population) were used to represent COVID-19 spread. Data sets also included 29 input variables for the corresponding six factors, containing data from 159 countries. The proposed model used a Multilayer Perceptron algorithm. The results show that each of the pre-mentioned factors significantly affects disease spread. Urban population, median age, life expectancy, numbers of medical doctors and nursing personnel, current health expenditure as a % of GDP, international health regulations capacity score, continent, literacy rate, governmental response stringency index, testing policy, internet usage %, human development index and GDP per capita were identified as significant. Taking early measures and adopting open public testing policies were recommended to policymakers in fighting pandemic diseases since the created scenarios on policy-based factors revealed their importance.

Suggested Citation

  • Sariyer, Gorkem & Mangla, Sachin Kumar & Kazancoglu, Yigit & Jain, Vranda & Ataman, Mustafa Gokalp, 2023. "Data-driven decision making for modelling covid-19 and its implications: A cross-country study," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:tefoso:v:197:y:2023:i:c:s0040162523005711
    DOI: 10.1016/j.techfore.2023.122886
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162523005711
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2023.122886?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Belhadi, Amine & Kamble, Sachin & Jabbour, Charbel Jose Chiappetta & Gunasekaran, Angappa & Ndubisi, Nelson Oly & Venkatesh, Mani, 2021. "Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    2. Hashem Abdullah AlNemer, 2023. "The COVID-19 pandemic and global food security: a bibliometric analysis and future research direction," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 50(5), pages 709-724, January.
    3. Benzidia, Smail & Makaoui, Naouel & Bentahar, Omar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    4. Sariyer, Gorkem & Kahraman, Serpil & Sözen, Mert Erkan & Ataman, Mustafa Gokalp, 2023. "Fiscal responses to COVID-19 outbreak for healthy economies: Modelling with big data analytics," Structural Change and Economic Dynamics, Elsevier, vol. 64(C), pages 191-198.
    5. Samayita Guha & Subodha Kumar, 2018. "Emergence of Big Data Research in Operations Management, Information Systems, and Healthcare: Past Contributions and Future Roadmap," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1724-1735, September.
    6. Lamba, Kuldeep & Singh, Surya Prakash, 2019. "Dynamic supplier selection and lot-sizing problem considering carbon emissions in a big data environment," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 573-584.
    7. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    8. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Shore, Adam & Ram, Pratibha, 2023. "Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    9. Nadezda Vasilievna SEDOVA & Lidia Sergeevna ARKHIPOVA & Darya Mikhailovna MELNIKOVA & Irina Fedorovna ALESHINA, 2022. "Digitalization Of Economy And Living Standards Of Population In Russian Regions," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(1), pages 47-65, June.
    10. Abdelrahman E. E. Eltoukhy & Ibrahim Abdelfadeel Shaban & Felix T. S. Chan & Mohammad A. M. Abdel-Aal, 2020. "Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations," IJERPH, MDPI, vol. 17(19), pages 1-25, September.
    11. Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "COVID-19 and stock returns: Evidence from the Markov switching dependence approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    12. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    13. Gupta, Shivam & Justy, Théo & Kamboj, Shampy & Kumar, Ajay & Kristoffersen, Eivind, 2021. "Big data and firm marketing performance: Findings from knowledge-based view," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    Full references (including those not matched with items on IDEAS)

    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. Munir, Muhammad Adeel & Hussain, Amjad & Farooq, Muhammad & Rehman, Ateekh Ur & Masood, Tariq, 2024. "Building resilient supply chains: Empirical evidence on the contributions of ambidexterity, risk management, and analytics capability," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    3. Chen, Pengyu & Chu, Zhongzhu & Zhao, Miao, 2024. "The Road to corporate sustainability: The importance of artificial intelligence," Technology in Society, Elsevier, vol. 76(C).
    4. Suyuan Luo & Tsan‐Ming Choi, 2022. "E‐commerce supply chains with considerations of cyber‐security: Should governments play a role?," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2107-2126, May.
    5. Fosso Wamba, Samuel & Queiroz, Maciel M. & Trinchera, Laura, 2024. "The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA," International Journal of Production Economics, Elsevier, vol. 268(C).
    6. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
    7. Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.
    8. ManMohan S. Sodhi & Zahra Seyedghorban & Hossein Tahernejad & Danny Samson, 2022. "Why emerging supply chain technologies initially disappoint: Blockchain, IoT, and AI," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2517-2537, June.
    9. Choi, Tsan-Ming & Feng, Lipan & Li, Rong, 2020. "Information disclosure structure in supply chains with rental service platforms in the blockchain technology era," International Journal of Production Economics, Elsevier, vol. 221(C).
    10. Subodha Kumar & Rakesh R. Mallipeddi, 2022. "Impact of cybersecurity on operations and supply chain management: Emerging trends and future research directions," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4488-4500, December.
    11. Han Bu & Zhou Xun & Sha Cai, 2024. "Big data and inter-firm wage disparities: theory and evidence from China," Economic Change and Restructuring, Springer, vol. 57(4), pages 1-36, August.
    12. Choi, Tsan-Ming & Guo, Shu & Luo, Suyuan, 2020. "When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    13. Chen, Jing & Pun, Hubert & Zhang, Qiao, 2023. "Eliminate demand information disadvantage in a supplier encroachment supply chain with information acquisition," European Journal of Operational Research, Elsevier, vol. 305(2), pages 659-673.
    14. Junaid, Muhammad & Zhang, Qingyu & Cao, Mei & Luqman, Adeel, 2023. "Nexus between technology enabled supply chain dynamic capabilities, integration, resilience, and sustainable performance: An empirical examination of healthcare organizations," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    15. Lu (Lucy) Yan, 2020. "The Kindness of Commenters: An Empirical Study of the Effectiveness of Perceived and Received Support for Weight‐Loss Outcomes," Production and Operations Management, Production and Operations Management Society, vol. 29(6), pages 1448-1466, June.
    16. Padhi, Sidhartha S. & Mukherjee, Soumyatanu & Edwin Cheng, T.C., 2024. "Optimal investment decision for industry 4.0 under uncertainties of capability and competence building for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 267(C).
    17. Jaehyeon Ju & Daegon Cho & Jae Kyu Lee & Jae‐Hyeon Ahn, 2021. "Can It Clean Up Your Inbox? Evidence from South Korean Anti‐spam Legislation," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2636-2652, August.
    18. Delke, Vincent & Schiele, Holger & Buchholz, Wolfgang & Kelly, Stephen, 2023. "Implementing Industry 4.0 technologies: Future roles in purchasing and supply management," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    19. Feng, Yunting & Lai, Kee-hung & Zhu, Qinghua, 2022. "Green supply chain innovation: Emergence, adoption, and challenges," International Journal of Production Economics, Elsevier, vol. 248(C).
    20. Yan, Ran & Liu, Yan & Wang, Shuaian, 2024. "A data-driven optimization approach to improving maritime transport efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 180(C).

    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:eee:tefoso:v:197:y:2023:i:c:s0040162523005711. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.