2011/05/18 by Paolo Bajardi, Alain Barrat, Fabrizio Natale +2 · 2 citations
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Environmental Science · Mathematics · Medicine · Physics and Astronomy · #Animal Disease Management and Epidemiology #Biology #Causality (physics) #Centrality #Complex network #Computer science #Dynamical systems theory #Evolutionary biology #Function (biology) #Mathematics #Network science #Node (physics) #Physics #Statistics #Wildlife Ecology and Conservation #Zoonotic diseases and public health #cond-mat.stat-mech #physics.soc-ph #q-bio.PE
paper · pdf · doi:10.1371/journal.pone.0019869
published as PLoS ONE 6(5): e19869(2011)
openalex publication_date 2011/05/18 · arxiv created 2011/05/19 · arxiv updated 2011/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions.