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The spatial propagation of many livestock infectious diseases criticallydepends on the animal movements among premises; so the knowledge of movementdata may help us to detect, manage and control an outbreak. The identificationof robust spreading features of the system is however hampered by the temporaldimension characterizing population interactions through movements. Traditionalcentrality measures do not provide relevant information as results stronglyfluctuate in time and outbreak properties heavily depend on geotemporal initialconditions. By focusing on the case study of cattle displacements in Italy, weaim at characterizing livestock epidemics in terms of robust features usefulfor planning and control, to deal with temporal fluctuations, sensitivity toinitial conditions and missing information during an outbreak. Through spatialdisease simulations, we detect spreading paths that are stable across differentinitial conditions, allowing the clustering of the seeds and reducing theepidemic variability. Paths also allow us to identify premises, calledsentinels, having a large probability of being infected and providing criticalinformation on the outbreak origin, as encoded in the clusters. This novelprocedure provides a general framework that can be applied to specificdiseases, for aiding risk assessment analysis and informing the design ofoptimal surveillance systems.

Optimizing surveillance for livestock disease spreading through animal movements