The Role Of Stochastic Processes In Understanding Epidemic Spread And Control

Abstract

In the light of the current global health crises, the study of the transmission of epidemics and the methods used to control them has become increasingly relevant. Understanding the random nature of disease transmission and the uncertainty in epidemic dynamics requires a firm grasp on stochastic processes, which play a significant role in this regard. The purpose of this study is to investigate the role that stochastic models, such as the Susceptible-Infected-Recovered (SIR) model and its variants, play in predicting the spread of infectious diseases and the consequences of intervention measures. The incorporation of randomness into these models allows them to more accurately represent real-world phenomena such as the occurrence of super-spreading episodes, changes in infection rates, and the stochastic extinction of illnesses. The implementation of these models in guiding public health policy, evaluating vaccination regimens, and managing epidemics through quarantine and social distancing measures is another topic that is investigated in this work. A comparison of deterministic and stochastic models reveals the advantages of the latter in terms of dealing with uncertainty and unusual occurrences, which ultimately leads to more effective strategies for controlling epidemics by contributing to more robust epidemic control measures.