Optimizing surveillance for livestock disease spreading through animal movements

TL;DR

This paper develops a framework using spatial disease simulations to identify stable spreading paths and sentinel premises in livestock disease outbreaks, improving surveillance and control despite temporal fluctuations and data uncertainties.

Contribution

It introduces a novel method for detecting robust spreading paths and sentinel premises in livestock disease networks, enhancing outbreak prediction and control strategies.

Findings

Stable spreading paths can be identified across different initial conditions.

Sentinel premises have a high probability of infection and indicate outbreak origins.

The framework aids in designing effective surveillance systems for livestock diseases.

Abstract

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.