Application of multiblock analysis to identify key determinants of PRRSV within farm stabilization: A European-wide survey
Abstract
Veterinary decision-making in infectious disease control often involves interacting factors across individual characteristics, professional practices, contextual, and regulatory levels. Traditional analytical approaches rarely capture this complexity. To address this challenge, we applied a regularized multiblock generalized canonical correlation analysis (rGCCA) to field survey data from European swine veterinarians in the context of porcine reproductive and respiratory syndrome virus (PRRSV) management to identify key factors associated with perceived success in PRRSV stabilization programs. A cross-sectional survey was conducted among 108 veterinarians from 22 European countries. A total of 123 variables were organized into six conceptual blocks: socio-professional characteristics, professional exchanges, evidence-based veterinary medicine (EBVM) engagement, regulation perceptions, PRRSV epidemiological context and management practices, and country. After univariate selection (p < 0.15), 71 variables were retained as potentially associated with perceived PRRSV stabilization success. A rGCCA model with two components per block was applied, followed by bootstrap resampling (500 iterations) to assess variable stability and block-to-block associations. The first component captured partial success in PRRSV stabilization programs, mainly related to insufficient cooperation and technical constraints. The second component reflected complete success and was strongly associated with country, professional exchanges, and contextual practices blocks. Notably, veterinarians reporting higher EBVM integration, greater teamwork frequency, and easier access to diagnostic and vaccination tools reported higher perceived stabilization success. Cross-block correlations highlighted significant interdependencies between geographical context, regulatory perception, and professional networking. This study demonstrates the potential of multiblock analysis to straighten out complex field data with cross-relationships between domains in veterinary epidemiology. The rGCCA framework provides an interpretable, replicable approach to explore multidimensional relationships and identify practitioner profiles associated with perceived disease stabilization success. These findings should be interpreted in light of the subjective nature of the perceived success outcome and do not necessarily reflect objective virological or production outcomes. Our methodological contribution supports the integration of data-driven, evidence-based strategies into veterinary epidemiology and disease control decision-making.
Related
- A dataset for the application of the Bayesian AMMI (B-AMMI) model to identify high-yielding and stable barley genotypes.
- A generalised Dynamic Energy Budget model including 3D shape changes for modelling fish growth: Application to the European anchovy Engraulis encrasicolus
- Why equal-time abundance correlations alone do not identify ecological interactions
- Spatially heterogenous distributions of dipteran abundances identify priority areas for haemosporidian parasite surveillance
- ENCODE guided WGS analysis can identify trait associated regulatory regions driven by rare variants
BibTeX
@article{costa2026application,
title = {{Application of multiblock analysis to identify key determinants of PRRSV within farm stabilization: A European-wide survey}},
author = {Charlotte Teixeira Costa and Arnaud Lebret and Stéphanie Bougeard and Nicolas Rose},
journal = {Preventive Veterinary Medicine},
year = {2026},
month = sep,
doi = {10.1016/j.prevetmed.2026.107008},
url = {https://anses.hal.science/anses-05764151v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Application of multiblock analysis to identify key determinants of PRRSV within farm stabilization: A European-wide survey
AU - Charlotte Teixeira Costa
AU - Arnaud Lebret
AU - Stéphanie Bougeard
AU - Nicolas Rose
PY - 2026
DA - 2026/09/25
JO - Preventive Veterinary Medicine
DO - 10.1016/j.prevetmed.2026.107008
UR - https://anses.hal.science/anses-05764151v1
AB - Veterinary decision-making in infectious disease control often involves interacting factors across individual characteristics, professional practices, contextual, and regulatory levels. Traditional analytical approaches rarely capture this complexity. To address this challenge, we applied a regularized multiblock generalized canonical correlation analysis (rGCCA) to field survey data from European swine veterinarians in the context of porcine reproductive and respiratory syndrome virus (PRRSV) management to identify key factors associated with perceived success in PRRSV stabilization programs. A cross-sectional survey was conducted among 108 veterinarians from 22 European countries. A total of 123 variables were organized into six conceptual blocks: socio-professional characteristics, professional exchanges, evidence-based veterinary medicine (EBVM) engagement, regulation perceptions, PRRSV epidemiological context and management practices, and country. After univariate selection (p < 0.15), 71 variables were retained as potentially associated with perceived PRRSV stabilization success. A rGCCA model with two components per block was applied, followed by bootstrap resampling (500 iterations) to assess variable stability and block-to-block associations. The first component captured partial success in PRRSV stabilization programs, mainly related to insufficient cooperation and technical constraints. The second component reflected complete success and was strongly associated with country, professional exchanges, and contextual practices blocks. Notably, veterinarians reporting higher EBVM integration, greater teamwork frequency, and easier access to diagnostic and vaccination tools reported higher perceived stabilization success. Cross-block correlations highlighted significant interdependencies between geographical context, regulatory perception, and professional networking. This study demonstrates the potential of multiblock analysis to straighten out complex field data with cross-relationships between domains in veterinary epidemiology. The rGCCA framework provides an interpretable, replicable approach to explore multidimensional relationships and identify practitioner profiles associated with perceived disease stabilization success. These findings should be interpreted in light of the subjective nature of the perceived success outcome and do not necessarily reflect objective virological or production outcomes. Our methodological contribution supports the integration of data-driven, evidence-based strategies into veterinary epidemiology and disease control decision-making.
SN - 0167-5877
SN - 0167-5877
ER -