  Livestock mobility, particularly that of small and large ruminants, is one of
the main pillars of production and trade in West Africa: livestock is moved
around in search of better grazing or sold in markets for domestic consumption
and for festival-related activities. These movements cover several thousand
kilometers and have the capability of connecting the whole West African region
thus facilitating the diffusion of many animal and zoonotic diseases. Several
factors shape mobility patterns even in normal years and surveillance systems
need to account for such changes. In this paper, we present a procedure based
on temporal network theory to identify possible sentinel locations using two
indicators: vulnerability (i.e. the probability of being reached by the
disease) and time of infection (i.e. the time of first arrival of the disease).
Using these indicators in our structural analysis of the changing network
enabled us to identify a set of nodes that could be used in an early warning
system. As a case study we simulated the introduction of F.A.S.T. (Foot and
Mouth Similar Transboundary) diseases in Senegal and used data taken from 2020
Sanitary certificates (LPS, laissez-passer sanitaire) issued by the Senegalese
Veterinary Services to reconstruct the national mobility network. Our analysis
showed that a static approach can significantly overestimate the speed and the
extent of disease propagation, whereas temporal analysis revealed that the
reachability and vulnerability of the different administrative departments
(used as nodes of the mobility network) change over the course of the year. For
this reason, several sets of sentinel nodes were identified in different
periods of the year, underlining the role of temporality in shaping patterns of
disease diffusion.
