Statistical and Mathematical Modeling in Biological Applications
Abstract: The spatial propagation of many livestock infectious diseases critically depends on the animal movements among livestock holdings. Models based on movement data are needed to simulate and analyse the spread of diseases and to determine the vulnerability of the livestock industry system to epidemic outbreaks. Identifying the most vulnerable elements of the system is crucial to disease control and important findings were provided by network approaches analyzing the structure of population interactions. The temporal dimension characterizing the movements pattern, however, introduces additional difficulties in assessing the consequences of an outbreak, thus hindering the development of efficient containment strategies. Here we address these aspects by considering a detailed temporal network describing cattle displacements among Italian premises. Through spatial disease simulations, we assess the role of initial conditions and classify the seeds into clusters leading to similar disease invasion paths. We put forward a novel procedure to identify premises that should be monitored as disease sentinels. Such sentinels are more likely to be infected if an outbreak occurs, and provide critical information on its origin. Our approach provides a general framework that can be applied to specific diseases, for aiding risk assessment analysis and informing the design of optimal surveillance systems.