We include (S)ARIMA, Kriging interpolation, SaTScan, LDFA, alpha and beta diversity analyses, UniFrac, and MVE in statistical models (Figure ). These models are more deterministic and often rooted in foundational principles and established, theoretical, and/or empirical relationships. For instance, SARIMA and Kriging interpolation can capture temporal and spatial patterns, respectively . Alpha and beta diversity analyses and UniFrac quantify microbial community diversity and compositional differences . These models typically operate under specific assumptions about the underlying data distribution or spatiotemporal relationships. By contrast, ML, especially deep learning, is more adaptive, learning patterns directly from the data without stringent assumptions .