2025/05/22 by Romario Gildas Foko Tiomela, Samson Adekola Alagbe, Tiomela, Romario Gildas Foko +7
Environmental Science · Mathematics · Medicine · #37A50 #37M25 #90C40 #92D30 #COVID-19 epidemiological studies #Ecosystem dynamics and resilience #FOS: Biological sciences #FOS: Physical sciences #Mathematical and Theoretical Epidemiology and Ecology Models #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE)
paper · pdf · doi:10.48550/arxiv.2505.21519
openalex publication_date 2025/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study introduces a comparative modeling framework using stationary and non-stationary transition probabilities within a Markov Decision Process (MDP) to assess COVID-19 disease dynamics. Stationary transition probabilities assume constant transition rates, while non-stationary transitions reflect time-dependent behaviors including policy interventions or behavioral changes. We develop a comprehensive compartmental model with transitions based on binomial and multinomial processes. Mathematical models for both stationary and non-stationary transition frameworks are developed and simulated over a 365-day period to emphasize dynamic variations in epidemic outcomes. Our findings highlight the significance of non-stationary modeling in accurately representing the dynamic characteristics of pandemic situations and provide recommendations for optimizing public health interventions under uncertainty. This comparative analysis offers useful information for epidemiological modeling and decision-making in dynamic risk environments.