Exploring Stochastic Approaches to Epidemic Modeling

Stochastic calculus has become an essential mathematical tool for modeling complex biological systems characterized by randomness and uncertainty. Unlike deterministic models, which may overlook the variability inherent in biological processes, stochastic calculus allows for explicitly incorporating noise and random fluctuations into the governing equations. This approach has proven particularly effective in fields such as population dynamics, gene regulation, neural activity, and the transmission of infectious diseases, where systems are influenced by random factors at both micro and macro scales. Researchers can use stochastic differential equations (SDEs) and Ito calculus to model biological systems' temporal evolution, accounting for deterministic components and stochastic disturbances. Applying stochastic calculus in biological modeling improves the precision of predictions and deepens our understanding of the mechanisms driving biological variability. As the discipline progresses, stochastic calculus is expected to play a growing role in enhancing our comprehension of complex biological phenomena and optimizing interventions in medicine, ecology, and biotechnology.