Stochastic and Markov Chain–Based Epidemic Models for COVID‐19: A Comprehensive Review of Predictive, Probabilistic, and Control Strategies

ABSTRACT To synthesize Markov chain and stochastic modeling approaches for predicting COVID‐19 transmission dynamics, incorporating factors like social distancing, vaccination, and data‐driven transitions. Reviewed studies employed continuous and discrete‐time Markov chains, stochastic compartmental models (SEIR, SIS), Monte Carlo simulations, and Bayesian inference. Data‐driven approaches integrated real‐time public health and wastewater data, accounting for population size, vaccination rates, and temporal correlations in uncertainties. Models accurately captured epidemic trends, predicting infection waves, incubation periods, and serial intervals. Stochastic models outperformed deterministic ones in small populations, while Bayesian and Monte Carlo methods enhanced uncertainty quantification. Vaccination and social distancing significantly reduced transmission rates. Markovian and stochastic models provide robust frameworks for epidemic forecasting, adaptable to diverse epidemiological conditions. These models inform public health strategies, improving outbreak management, intervention planning, and resource allocation in pandemics.