  In aquaculture, disease spread models often neglect the dynamic interactions
between farms, hindering accuracy. This study enhances the Katz index (KI) to
incorporate spatial and temporal patterns of fish movement, improving the
prediction of farms susceptible to disease via live fish transfers. We modified
the Katz index to create models like the Weighted Katz Index (WKI), Edge
Weighted Katz Index (EWKI), and combined models (e.g., KIEWKI). These
incorporate spatial distances and temporal movement patterns for a
comprehensive aquaculture network connection prediction framework. Model
performance was evaluated using precision, recall, F1-scores, AUPR, and AUROC.
The EWKI model significantly outperformed the traditional KI and other
variations. It achieved high precision (0.988), recall (0.712), F1-score
(0.827), and AUPR (0.970). Combined models (KIEWKI, WKIEWKI) approached, but
couldn't surpass, EWKI performance. This study highlights the value of
extending Katz index models to improve disease spread predictions in
aquaculture networks. The EWKI model's performance demonstrates an innovative
and flexible approach to tackling spatial challenges within network analysis.
