This document summarizes a presentation about analyzing cattle trade networks from 13 European countries and predicting their vulnerability to disease outbreaks. It discusses how past works have analyzed cattle networks in individual countries but a comprehensive study across multiple countries was still needed. The presentation will share a collaborative platform that allows effective comparative analysis of cattle trade data from 13 European countries by bringing analysis code to the data instead of sharing the data directly. It extracts indicators from the data and shows these can predict a country's vulnerability to the spread of various infections. The work is a first step toward risk assessment tools to help monitoring policies with minimal data sharing.

1. 1EuFMD | Open Session special edition | #OS20se
Analyzing cattle trade networks from 13
European countries, and predicting their
vulnerability to outbreaks
Professor Eugenio Valdano
INSERM, Sorbonne Université

2. 2EuFMD | Open Session special edition | #OS20se
Trade-driven animal displacements among cattle holdings drive the likelihood, shape, and
speed of outbreaks. Past works analyzed cattle networks in several countries, highlighting
complex interactions between structure, function, and dynamics. A comprehensive study,
linking features of cattle trade networks to their vulnerability to the spread of infectious
diseases, is however still missing. Such study requires large datasets across different
countries and years, to highlight global markers of cattle trade networks, as well as region-
specific patterns. The main problem is data availability: cattle trade data are not public,
and their access requires ad hoc agreements. I will present a collaborative platform that,
using a bring code to the data approach, overcomes the strict regulations preventing data
sharing, and allows an effective comparative analysis. Analyzing data from 13 European
countries, we extract a set of synthetic indicators that quantifies shared features, and
differences, among countries, and across years. We then show that these indicators can
predict vulnerability of a specific national market to the spatial spread of a wide range of
infections. Our work is a first step to building data-driven risk assessment tools that can be
integrated into monitoring policies, with minimal data sharing requirements.

3. eufmdvirtual.com
8, 10, 15, 17 December 2020
EuFMD / Open Session