PLOS ONE, 2019
Infections can spread among livestock notably because infected animals can be brought to
uncontaminated holdings, therefore exposing a new group of susceptible animals to the dis-
ease. As a consequence, the structure and dynamics of animal trade networks is a major
focus of interest to control zoonosis. We investigate the impact of the chronology of animal
trades on the dynamics of the process. Precisely, in the context of a basic SI model spread-
ing, we measure on the French database of bovine transfers to what extent a snapshot-
based analysis of the cattle trade networks overestimates the epidemic risks. We bring into
light that an analysis taking into account the chronology of interactions would give a much
more accurate assessment of both the size and speed of the process. For this purpose, we
model data as a temporal network that we analyze using the link stream formalism in order
to mix structural and temporal aspects. We also show that in this dataset, a basic SI spread-
ing comes down in most cases to a simple two-phases scenario: a waiting period, with few
contacts and low activity, followed by a linear growth of the number of infected holdings.
Using this portrait of the spreading process, we identify efficient strategies to control a
potential outbreak, based on the identification of specific elements of the link stream which
have a higher probability to be involved in a spreading process.