Plant disease surveillance is essential for the management of disease outbreaks that pose significant threats to agricultural sustainability. In this study, we present a novel sequential point process model designed for disease surveillance. The model incorporates self-interaction mechanisms to account for the influence of the process’ history. To analyze the dynamics of the model, we propose new sequential summary statistics that extend traditional point process methods to scenarios where sequential interactions are critical. This model serves a dual purpose: it is employed both to propose novel and efficient sampling designs, and to characterize existing sampling schemes, implemented in real-world situations, through parameter inference.
Landscape and environmental drivers of plant health decline during an active invasion of Xylella fastidiosa Wells et al. in Mediterranean agroecosystems
Invasive plant pathogens, such as Xylella fastidiosa (Xf), pose a major threat to global agricultural and natural ecosystems, yet their impacts are rarely assessed across agro–natural interfaces where plant diversity, landscape structure and environmental stress...