Kernel Density Estimation (KDE) as a tool to enhance bovine tuberculosis surveillance in Santa Catarina, Brazil.

Abstract

In areas with low bovine tuberculosis (bTB) prevalence, such as Santa Catarina state, Brazil, effective surveillance is essential for disease eradication. Current strategies may miss high-risk farms by inadequately considering spatial risk factors. This study used Kernel Density Estimation (KDE) to analyze spatial risk patterns in Santa Catarina, Brazil, leveraging the official veterinary service's (CIDASC's Sigen+ database) farm data, testing history, and animal movement records. Results revealed that while existing surveillance targets many high-risk areas, some remain unmonitored. Practices such as on-farm slaughter and insufficient movement testing create vulnerabilities that can hinder bTB detection. Integrating KDE-derived risk maps into the current surveillance efforts can improve targeted resource allocation and disease control. This study demonstrated the value of spatial risk analysis for enhancing bTB surveillance in Santa Catarina state, offering a strategic tool to support CIDASC's eradication efforts and serving as a model for other regions seeking to strengthen their surveillance programs.