The spatial propagation of many livestock infectious
diseases critically depends on the animal movements among premises; so
the knowledge of movement data may help us to detect, manage and
control an outbreak. The identification of robust spreading features
of the system is however hampered by the temporal dimension
characterizing population interactions through movements. Traditional
centrality measures do not provide relevant information as results
strongly fluctuate in time and outbreak properties heavily depend on
geotemporal initial conditions. By focusing on the case study of
cattle displacements in Italy, we aim at characterizing livestock
epidemics in terms of robust features useful for planning and control,
to deal with temporal fluctuations, sensitivity to initial conditions
and missing information during an outbreak. Through spatial disease
simulations, we detect spreading paths that are stable across
different initial conditions, allowing the clustering of the seeds and
reducing the epidemic variability. Paths also allow us to identify
premises, called sentinels, having a large probability of being
infected and providing critical information on the outbreak origin, as
encoded in the clusters. This novel procedure provides a general
framework that can be applied to specific diseases, for aiding risk
assessment analysis and informing the design of optimal surveillance
systems.
Joint work with Paolo Bajardi (ISI Foundation, Italy), Lara Savini
(Istituto Zooprofilattico Sperimentale Abruzzo-Molise, Italy),
Vittoria Colizza (INSERM, France & ISI Foundation, Italy)