Epidemiological studies benefit from change-point detection to identify shifts in disease patterns over time. Detecting changes in disease incidence helps us act quickly and allocate resources better. In this talk, we present a stochastic pro-

cess suitable for the data with a cyclic mean-reverting behavior. Here are the key contributions: Firstly, the mean-reverting term is a periodic function, constantly fluctuating. Our emphasis lies in detecting the change-point in drift parameters

within the specialized framework of the SDE sequentially, rather than focusing on changes in mean, variance, or covariance. Additionally, we introduce several detectors designed to identify the location of the change-point and provide the asymptotic properties of these detectors under both the null and alternative hypotheses. This type of SDE can be applied to the SIS epidemic model. Our work offers both theoretical advancement and practical insights into this domain.