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An extended robust mathematical model to project the course of COVID-19 epidemic in Iran

Author

Listed:
  • Reza Lotfi

    (Yazd University
    Behineh Gostar Sanaye Arman)

  • Kiana Kheiri

    (Shahid Beheshti University)

  • Ali Sadeghi

    (Yazd University)

  • Erfan Babaee Tirkolaee

    (Istinye University)

Abstract

This research develops a regression-based Robust Optimization (RO) approach to efficiently predict the number of patients with confirmed infection caused by the recent Coronavirus Disease (COVID-19). The main idea is to study the dynamics of the COVID-19 outbreak at the first stage and then provide efficient insights to estimate the necessary resources accordingly. The convex RO with Mean Absolute Deviation (MAD) objective function is utilized to project the course of COVID-19 epidemic in Iran. To validate the performance of the suggested model, a real-case study is investigated and compared to several well-known forecasting models including Simple Moving Average, Exponential Moving Average, Weighted Moving Average and Exponential Smoothing with Trend Adjustment models. Furthermore, the effect of parameter uncertainties is examined using a set of sensitivity analyses. The results demonstrate that by increasing the degree (coefficient) of regression up to 8, MAD value decreases to 1378.12, and consequently, the corresponding equation becomes more accurate. On the other hand, from the 8th degree onwards, MAD value follows an upward trend. Furthermore, by increasing the level of regression uncertainty, MAD value follows a downward trend to reach 1309.28 and the estimation accuracy of the model increases accordingly. Finally, our proposed model achieves the least MAD and the greatest correlation coefficient against the other models.

Suggested Citation

  • Reza Lotfi & Kiana Kheiri & Ali Sadeghi & Erfan Babaee Tirkolaee, 2024. "An extended robust mathematical model to project the course of COVID-19 epidemic in Iran," Annals of Operations Research, Springer, vol. 339(3), pages 1499-1523, August.
    • Handle: RePEc:spr:annopr:v:339:y:2024:i:3:d:10.1007_s10479-021-04490-6
    DOI: 10.1007/s10479-021-04490-6
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    References listed on IDEAS

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