| Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model |
| Paper ID : 1140-ICEEM2023 (R1) |
| Authors |
|
Sayed kenawy *1, Mahmoud Elshabrawy2, Marwa M. Eid3, Abdelaziz A. Abdelhamid4, Abdelhameed Ibrahim5 1Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology 2Department of Communications and Electronics Delta Higher Institute of Engineering and Technology, Mansoura, Egypt 3Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura 35111, Egypt 4Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University, Cairo, Egypt 5Computer Engineering and Control Systems Department Faculty of Engineering University Mansoura, Egypt |
| Abstract |
| This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search crossvalidation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model’s consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model’s predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic’s course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models. |
| Keywords |
| Dipper throated optimization, SARIMAX model, ant colony optimization, Monkeypox |
| Status: Accepted |