In epidemiological studies, stationarity implies that the statistical properties of the time series, such as mean and variance, do not change over time. This assumption is critical for many time series analysis techniques. For instance, models like ARIMA (AutoRegressive Integrated Moving Average) require the data to be stationary. Non-stationary data can lead to misleading results and poor forecasts, impacting public health decisions and interventions.