Rapid decision-making during exotic animal disease outbreaks necessitates the early characterisation of transmission dynamics; however, in practice, a strategic balance between accuracy and speed is required. This study presents a simplified and practical approach for field response, leveraging initial kernel estimates from spatial data to predict outbreak risks without the need for high-dimensional data or complex models. Using the 2001 United Kingdom foot-and-mouth disease outbreak as a case study, we identified the minimum data requirements for estimating stable transmission kernels during the early stages of an outbreak and evaluated their applicability as short-term predictive tools for field operations. Focusing on the five most affected regions, we estimated the optimal kernels on a weekly basis during the first month and monthly thereafter, validating their stability against full-outbreak datasets. Furthermore, we analysed the predictive accuracy for disease spatial spread within a seven-day window by calculating infectious pressure from these weekly updated kernels. Our analysis revealed that the power law type 2 kernel was consistently selected as the best fitting kernel across all study regions. Furthermore, the results demonstrated that reliable kernel estimation became feasible within clusters as early as seven days post-notification, or once a threshold of at least ten cumulative infected premises was reached. The kernel-based risk estimation had high sensitivity and negative predictive value, reliably identifying ‘low-risk’ areas. These findings demonstrate that even with minimal initial data and without complex parameters, it is possible to generate practically valid biosecurity guidelines. The proposed real-time kernel approach serves as a strategic tool to support evidence-based, rapid decision-making during the early phases of an animal disease outbreak, enabling the prioritised allocation of limited resources and the flexible setting of intervention thresholds.