Journal of Mathematics, Journal Year: 2024, Volume and Issue: 2024(1)
Published: Jan. 1, 2024
Background: COVID‐19 has posed substantial challenges to healthcare systems globally, emphasizing the need for robust epidemiological tracking, particularly in regions like Saudi Arabia where distinct variations case rates necessitate specialized public health strategies. Aims and Objectives: This study aims identify most effective model predicting trends by analyzing daily time‐to‐event data across six regions. The goal is enhance resource allocation intervention strategies gaining a deeper understanding of epidemic’s dynamics developing reliable method early detection trend changes prompt timely actions. Research Gap: Previous studies have predominantly utilized simpler statistical models such as log‐normal, Gamma, Weibull distributions, which often fall short accurately describing complex behaviors during peak times periods periodicity. addresses these shortcomings employing advanced trigonometrically enhanced models. Methodology: To from various spanning 2020 2023, we employed new trigonometric distribution trigonometric‐generated P‐class distributions. Our methodological approach included comprehensive validation process, utilizing comparative analysis seven estimation techniques. Maximum likelihood proved be overall. We also adapted specific classical techniques fit unique characteristics each region: Cramer von Mises Abu ‘Urwah, Al‐Aridah, Al Bada’I; Anderson–Darling Abha; maximum product spacing Ad Dammam; ‘Arish. tailored allowed detailed regional data, enhancing accuracy relevance our modeling outcomes. Conclusions: effectively modeled cases Arabia, demonstrating superior capturing epidemic dynamics. highlights distribution’s capability detect significant periodic fluctuations, response Its successful application underscores value research pandemic management.
Language: Английский