Understanding nonstationarity is crucial for effective disease modeling and prediction. Traditional models often assume stationary processes, which can lead to inaccurate forecasts and suboptimal public health responses. Recognizing nonstationarity allows for the development of more sophisticated models that can adapt to changing conditions and improve epidemiological surveillance.
