Cattle trade networks in Europe
Eugenio Valdano
(1)
,
Luca Ferreri
(2)
,
Alexandre Darbon
(3, 4)
,
Chiara Poletto
(3, 4)
,
Lara Savini
(5)
,
Carla Ippoliti
(5)
,
Armando Giovannini
(5)
,
Peter Brommesson
(6)
,
Stefan Sellmann
(6)
,
Uno Wennergren
(6)
,
Andreas Koher
(7)
,
Jason Basset
(7)
,
Hartmut H. K. Lentz
(8)
,
Vitaly Belik
(9)
,
Philipp Hövel
(10)
,
Akos Jozwiak
(11)
,
Jessica Enright
(12)
,
Rowland R. Kao
(12)
,
Pauline Ezanno
(13)
,
Gael Beaunée
(13, 14)
,
Elisabeta Vergu
(14)
,
Reinhard Fuchs
(15)
,
Klemens Fuchs
(15)
,
Beatriz Vidondo
(16)
,
Ana Pascual
(17)
,
Emily Courcier
(17)
,
Vittoria Colizza
(3, 4, 18)
1
Departament d’Enginyeria Informàtica i Matemàtiques
2 Data Driven Innovation
3 UPMC - Université Pierre et Marie Curie - Paris 6
4 iPLESP - Institut Pierre Louis d'Epidémiologie et de Santé Publique
5 IZSAM - Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise Guiseppe Caporale
6 LIU - Linköping University
7 Institut für Theoretische Physik [Berlin]
8 Institute of Epidemiology
9 Institute for Veterinary Epidemiology and Biostatistics
10 ITP - Institut für Theoretische Physik [Hannover]
11 NEBIH - National Food Chain Safety Office
12 Boyd Orr Centre for Population and Ecosystem Health
13 BIOEPAR - Biologie, Epidémiologie et analyse de risque en Santé Animale
14 MaIAGE - Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas]
15 Austrian Agency for Health and Food Safety
16 Veterinary Public Health Institute
17 Department of Agriculture and Environmental Affairs
18 Institute for Scientific Interchange
2 Data Driven Innovation
3 UPMC - Université Pierre et Marie Curie - Paris 6
4 iPLESP - Institut Pierre Louis d'Epidémiologie et de Santé Publique
5 IZSAM - Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise Guiseppe Caporale
6 LIU - Linköping University
7 Institut für Theoretische Physik [Berlin]
8 Institute of Epidemiology
9 Institute for Veterinary Epidemiology and Biostatistics
10 ITP - Institut für Theoretische Physik [Hannover]
11 NEBIH - National Food Chain Safety Office
12 Boyd Orr Centre for Population and Ecosystem Health
13 BIOEPAR - Biologie, Epidémiologie et analyse de risque en Santé Animale
14 MaIAGE - Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas]
15 Austrian Agency for Health and Food Safety
16 Veterinary Public Health Institute
17 Department of Agriculture and Environmental Affairs
18 Institute for Scientific Interchange
Eugenio Valdano
- Fonction : Auteur
- PersonId : 1198243
- IdHAL : eugenio-valdano
- ORCID : 0000-0002-9246-6195
Chiara Poletto
- Fonction : Auteur
- PersonId : 761801
- ORCID : 0000-0002-4051-1716
Pauline Ezanno
- Fonction : Auteur
- PersonId : 747264
- IdHAL : pauline-ezanno
- ORCID : 0000-0002-0034-8950
- IdRef : 177334991
Gael Beaunée
- Fonction : Auteur
- PersonId : 737834
- IdHAL : gael-beaunee
- ORCID : 0000-0002-0002-2627
Elisabeta Vergu
- Fonction : Auteur
- PersonId : 1205082
Vittoria Colizza
- Fonction : Auteur
- PersonId : 755791
- ORCID : 0000-0002-2113-2374
- IdRef : 189420952
Résumé
Diseases affecting farmed cattle compromise both human and animal health and welfare, and represent a major cause of loss in economic revenue. Their spread is known to be driven, or at least facilitated, by animal displacements among livestock holdings, both within and across countries. As a result, studying the networks of animal movements is a key step in devising new prevention and containment strategies. Past works have already analyzed cattle networks in several European countries, highlighting complex interactions between topology, function and dynamics at different spatial and time resolutions. A comprehensive study, showing the impact of country-specific driving factors on network evolution and topology, is however still missing. By using data from several European countries, and focusing on the features relevant for the spread of infections, we perform a comparative analysis to highlight both general and country-specific patterns. We find that coarse-graining the structure into statistical distributions of centrality measures is an effective way to highlight the properties shared by all networks, which represent the fingerprint of a livestock market. The situation dramatically changes when we zoom in to the microscopic structure, as we find several country-specific characteristics, especially in temporal evolution. This twofold behavior suggests that on one hand it is possible to identify several global patterns in the ways animal disease spread, which can then be applied to countries for which data are unavailable, or incomplete. On the other hand, resolved country-specific data are needed when devising tailored and targeted intervention strategies