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This is a Quantitative Study study.

Investigation of Deterministic and Stochastic Sir Epidemiological Model With Nonlinear Incidence Rate

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Analysis explores dynamics of disease transmission in populations with deterministic and stochastic frameworks, suggesting more robust models.

The incorporation of stochastic elements into epidemiological models enhances accuracy and relevance for disease spread.
Numerical simulations confirm findings from both deterministic and stochastic models, providing a clearer picture of epidemic dynamics.
Analyzing transition probabilities allows for probabilistic insights into disease propagation, essential for effective public health strategies.
This study highlights the importance of considering variability and uncertainty in epidemiological research for better predictive capabilities.

Nazlipinar et al. (2025) studied this question.

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Analysis explores dynamics of disease transmission in populations with deterministic and stochastic frameworks, suggesting more robust models.