Integer-valued time series models play an essential role in the analysis of dependent count data. One of the main challenges in these models is to detect structural changes over time. These changes may be caused by sudden interventions such as policy changes, pandemics, or system failures. In this paper, the empirical likelihood method is used to detect structural changes in a class of INAR(1) processes. This method is a tool for early warning of structural changes in these processes. Using simulation, the empirical sizes and powers of the test are calculated for different sample sizes, and the test's performance is investigated. Finally, the practical efficiency of the test is investigated by identifying the change point in two real datasets: the number of robberies and the number of COVID-19 deaths.