Prediction of pig trade movements in different European production systems using exponential random graph models

Anne Relun, Vladimir Grosbois, Tsviatko Alexandrov, Jose M. Sánchez-Vizcaíno, Agnes Waret-Szkuta, Sophie Molia, Eric Marcel Charles Etter, Beatriz Martinez Lopez

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

In most European countries, data regarding movements of live animals are routinely collected and can greatly aid predictive epidemic modeling. However, the use of complete movements' dataset to conduct policy-relevant predictions has been so far limited by the massive amount of data that have to be processed (e.g., in intensive commercial systems) or the restricted availability of timely and updated records on animal movements (e.g., in areas where small-scale or extensive production is predominant). The aim of this study was to use exponential random graph models (ERGMs) to reproduce, understand, and predict pig trade networks in different European production systems. Three trade networks were built by aggregating movements of pig batches among premises (farms and trade operators) over 2011 in Bulgaria, Extremadura (Spain), and Côtes-d'Armor (France), where small-scale, extensive, and intensive pig production are predominant, respectively. Three ERGMs were fitted to each network with various demographic and geographic attributes of the nodes as well as six internal network configurations. Several statistical and graphical diagnostic methods were applied to assess the goodness of fit of the models. For all systems, both exogenous (attribute-based) and endogenous (network-based) processes appeared to govern the structure of pig trade network, and neither alone were capable of capturing all aspects of the network structure. Geographic mixing patterns strongly structured pig trade organization in the small-scale production system, whereas belonging to the same company or keeping pigs in the same housing system appeared to be key drivers of pig trade, in intensive and extensive production systems, respectively. Heterogeneous mixing between types of production also explained a part of network structure, whichever production system considered. Limited information is thus needed to capture most of the global structure of pig trade networks. Such findings will be useful to simplify trade networks analysis and better inform European policy makers on risk-based and more cost-effective prevention and control against swine diseases such as African swine fever, classical swine fever, or porcine reproductive and respiratory syndrome.

Original languageEnglish (US)
Article number27
JournalFrontiers in Veterinary Science
Volume4
Issue numberMAR
DOIs
StatePublished - Mar 3 2017

Fingerprint

production technology
Swine
swine
prediction
African Swine Fever
Swine Diseases
Porcine Reproductive and Respiratory Syndrome
Classical Swine Fever
swine housing
African swine fever
porcine reproductive and respiratory syndrome
Bulgaria
swine diseases
hog cholera
Administrative Personnel
Spain
diagnostic techniques
France
animals
demographic statistics

Keywords

  • ERGM
  • Infectious diseases
  • Livestock contact networks
  • Network modeling
  • Risk-based surveillance

ASJC Scopus subject areas

  • veterinary(all)

Cite this

Prediction of pig trade movements in different European production systems using exponential random graph models. / Relun, Anne; Grosbois, Vladimir; Alexandrov, Tsviatko; Sánchez-Vizcaíno, Jose M.; Waret-Szkuta, Agnes; Molia, Sophie; Charles Etter, Eric Marcel; Martinez Lopez, Beatriz.

In: Frontiers in Veterinary Science, Vol. 4, No. MAR, 27, 03.03.2017.

Research output: Contribution to journalArticle

Relun, A, Grosbois, V, Alexandrov, T, Sánchez-Vizcaíno, JM, Waret-Szkuta, A, Molia, S, Charles Etter, EM & Martinez Lopez, B 2017, 'Prediction of pig trade movements in different European production systems using exponential random graph models', Frontiers in Veterinary Science, vol. 4, no. MAR, 27. https://doi.org/10.3389/fvets.2017.00027
Relun, Anne ; Grosbois, Vladimir ; Alexandrov, Tsviatko ; Sánchez-Vizcaíno, Jose M. ; Waret-Szkuta, Agnes ; Molia, Sophie ; Charles Etter, Eric Marcel ; Martinez Lopez, Beatriz. / Prediction of pig trade movements in different European production systems using exponential random graph models. In: Frontiers in Veterinary Science. 2017 ; Vol. 4, No. MAR.
@article{2e621f9f5669411391f06057e5cc9387,
title = "Prediction of pig trade movements in different European production systems using exponential random graph models",
abstract = "In most European countries, data regarding movements of live animals are routinely collected and can greatly aid predictive epidemic modeling. However, the use of complete movements' dataset to conduct policy-relevant predictions has been so far limited by the massive amount of data that have to be processed (e.g., in intensive commercial systems) or the restricted availability of timely and updated records on animal movements (e.g., in areas where small-scale or extensive production is predominant). The aim of this study was to use exponential random graph models (ERGMs) to reproduce, understand, and predict pig trade networks in different European production systems. Three trade networks were built by aggregating movements of pig batches among premises (farms and trade operators) over 2011 in Bulgaria, Extremadura (Spain), and C{\^o}tes-d'Armor (France), where small-scale, extensive, and intensive pig production are predominant, respectively. Three ERGMs were fitted to each network with various demographic and geographic attributes of the nodes as well as six internal network configurations. Several statistical and graphical diagnostic methods were applied to assess the goodness of fit of the models. For all systems, both exogenous (attribute-based) and endogenous (network-based) processes appeared to govern the structure of pig trade network, and neither alone were capable of capturing all aspects of the network structure. Geographic mixing patterns strongly structured pig trade organization in the small-scale production system, whereas belonging to the same company or keeping pigs in the same housing system appeared to be key drivers of pig trade, in intensive and extensive production systems, respectively. Heterogeneous mixing between types of production also explained a part of network structure, whichever production system considered. Limited information is thus needed to capture most of the global structure of pig trade networks. Such findings will be useful to simplify trade networks analysis and better inform European policy makers on risk-based and more cost-effective prevention and control against swine diseases such as African swine fever, classical swine fever, or porcine reproductive and respiratory syndrome.",
keywords = "ERGM, Infectious diseases, Livestock contact networks, Network modeling, Risk-based surveillance",
author = "Anne Relun and Vladimir Grosbois and Tsviatko Alexandrov and S{\'a}nchez-Vizca{\'i}no, {Jose M.} and Agnes Waret-Szkuta and Sophie Molia and {Charles Etter}, {Eric Marcel} and {Martinez Lopez}, Beatriz",
year = "2017",
month = "3",
day = "3",
doi = "10.3389/fvets.2017.00027",
language = "English (US)",
volume = "4",
journal = "Frontiers in Veterinary Science",
issn = "2297-1769",
publisher = "Frontiers Media S. A.",
number = "MAR",

}

TY - JOUR

T1 - Prediction of pig trade movements in different European production systems using exponential random graph models

AU - Relun, Anne

AU - Grosbois, Vladimir

AU - Alexandrov, Tsviatko

AU - Sánchez-Vizcaíno, Jose M.

AU - Waret-Szkuta, Agnes

AU - Molia, Sophie

AU - Charles Etter, Eric Marcel

AU - Martinez Lopez, Beatriz

PY - 2017/3/3

Y1 - 2017/3/3

N2 - In most European countries, data regarding movements of live animals are routinely collected and can greatly aid predictive epidemic modeling. However, the use of complete movements' dataset to conduct policy-relevant predictions has been so far limited by the massive amount of data that have to be processed (e.g., in intensive commercial systems) or the restricted availability of timely and updated records on animal movements (e.g., in areas where small-scale or extensive production is predominant). The aim of this study was to use exponential random graph models (ERGMs) to reproduce, understand, and predict pig trade networks in different European production systems. Three trade networks were built by aggregating movements of pig batches among premises (farms and trade operators) over 2011 in Bulgaria, Extremadura (Spain), and Côtes-d'Armor (France), where small-scale, extensive, and intensive pig production are predominant, respectively. Three ERGMs were fitted to each network with various demographic and geographic attributes of the nodes as well as six internal network configurations. Several statistical and graphical diagnostic methods were applied to assess the goodness of fit of the models. For all systems, both exogenous (attribute-based) and endogenous (network-based) processes appeared to govern the structure of pig trade network, and neither alone were capable of capturing all aspects of the network structure. Geographic mixing patterns strongly structured pig trade organization in the small-scale production system, whereas belonging to the same company or keeping pigs in the same housing system appeared to be key drivers of pig trade, in intensive and extensive production systems, respectively. Heterogeneous mixing between types of production also explained a part of network structure, whichever production system considered. Limited information is thus needed to capture most of the global structure of pig trade networks. Such findings will be useful to simplify trade networks analysis and better inform European policy makers on risk-based and more cost-effective prevention and control against swine diseases such as African swine fever, classical swine fever, or porcine reproductive and respiratory syndrome.

AB - In most European countries, data regarding movements of live animals are routinely collected and can greatly aid predictive epidemic modeling. However, the use of complete movements' dataset to conduct policy-relevant predictions has been so far limited by the massive amount of data that have to be processed (e.g., in intensive commercial systems) or the restricted availability of timely and updated records on animal movements (e.g., in areas where small-scale or extensive production is predominant). The aim of this study was to use exponential random graph models (ERGMs) to reproduce, understand, and predict pig trade networks in different European production systems. Three trade networks were built by aggregating movements of pig batches among premises (farms and trade operators) over 2011 in Bulgaria, Extremadura (Spain), and Côtes-d'Armor (France), where small-scale, extensive, and intensive pig production are predominant, respectively. Three ERGMs were fitted to each network with various demographic and geographic attributes of the nodes as well as six internal network configurations. Several statistical and graphical diagnostic methods were applied to assess the goodness of fit of the models. For all systems, both exogenous (attribute-based) and endogenous (network-based) processes appeared to govern the structure of pig trade network, and neither alone were capable of capturing all aspects of the network structure. Geographic mixing patterns strongly structured pig trade organization in the small-scale production system, whereas belonging to the same company or keeping pigs in the same housing system appeared to be key drivers of pig trade, in intensive and extensive production systems, respectively. Heterogeneous mixing between types of production also explained a part of network structure, whichever production system considered. Limited information is thus needed to capture most of the global structure of pig trade networks. Such findings will be useful to simplify trade networks analysis and better inform European policy makers on risk-based and more cost-effective prevention and control against swine diseases such as African swine fever, classical swine fever, or porcine reproductive and respiratory syndrome.

KW - ERGM

KW - Infectious diseases

KW - Livestock contact networks

KW - Network modeling

KW - Risk-based surveillance

UR - http://www.scopus.com/inward/record.url?scp=85020701935&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020701935&partnerID=8YFLogxK

U2 - 10.3389/fvets.2017.00027

DO - 10.3389/fvets.2017.00027

M3 - Article

AN - SCOPUS:85020701935

VL - 4

JO - Frontiers in Veterinary Science

JF - Frontiers in Veterinary Science

SN - 2297-1769

IS - MAR

M1 - 27

ER -