ArticleAnnals of operations research2022
An extended robust mathematical model to project the course of COVID-19 epidemic in Iran.
Article in Annals of operations research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Responsible optimization of long-term production scheduling in open-pit mines.Scientific reports · 2026Article
- GA BP prediction model for energy consumption of steel rolling reheating furnace.Scientific reports · 2025Article
- Mathematical modeling for Delta and Omicron variant of SARS-CoV-2 transmission dynamics in Greece.Infectious Disease Modelling · 2023Article
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- CovTiNet: Covid text identification network using attention-based positional embedding feature fusion.Neural computing & applications · 2023Article
- A robust and resilience machine learning for forecasting agri-food production.Scientific reports · 2022Article
- Designing a sustainable reverse supply chain network for COVID-19 vaccine waste under uncertainty.Computers & industrial engineering · 2022Article
- Comparison of hospital service performances under COVID-19 pandemics for pilot regions with low vaccination rates.Expert systems with applications · 2022Article
- Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest recursive feature elimination algorithm.Annals of operations research · 2022Article
- Article
- A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item.Annals of operations research · 2022Article
- Mathematical COVID-19 model with vaccination: a case study in Saudi Arabia.PeerJ. Computer science · 2022Article
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4 authors.
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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.
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