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An Intelligent Algorithm to Predict GDP Rate and Find a Relationship Between COVID-19 Outbreak and Economic Downturn

Author

Listed:
  • Amir Masoud Rahmani

    (National Yunlin University of Science and Technology)

  • Seyedeh Yasaman Hosseini Mirmahaleh

    (Lille University)

Abstract

With the spread of COVID-19, economic damages are challenging for governments and people’s livelihood besides its dangerous and negative impact on humanity's health, which can be led to death. Various health guidelines have been proposed to tackle the virus outbreak including quarantine, restriction rules to imports, exports, migrations, and tourist arrival that were affected by economic depression. Providing an approach to predict the economic situation has a highlighted role in managing crisis when a country faces a problem such as a disease epidemic. We propose an intelligent algorithm to predict the economic situation that utilizes neural networks (NNs) to satisfy the aim. Our work estimates correlation coefficient based on the spearman method between gross domestic product rate (GDPR) and other economic statistics to find effective parameters on growing up and falling GDPR and also determined the NNs’ inputs. We study the reported economic and disease statistics in Germany, India, Australia, and Thailand countries to evaluate the algorithm’s efficiency in predicting economic situation. The experimental results demonstrate the prediction accuracy of approximately 96% and 89% for one and more months ahead, respectively. Our method can help governments to present efficient policies for preventing economic damages.

Suggested Citation

  • Amir Masoud Rahmani & Seyedeh Yasaman Hosseini Mirmahaleh, 2024. "An Intelligent Algorithm to Predict GDP Rate and Find a Relationship Between COVID-19 Outbreak and Economic Downturn," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1001-1020, March.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:3:d:10.1007_s10614-022-10332-9
    DOI: 10.1007/s10614-022-10332-9
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    References listed on IDEAS

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