Network analysis as a preliminary screening tool for food safety risk assessment using European Union RASFF notifications from 2022 to 2024

Food safety remains a core priority in the European Union, where dense trade networks demand rapid detection and coordinated response. The Rapid Alert System for Food and Feed (RASFF) enables near-real-time exchange of hazard information among EU member states and associated countries. This study analyses 14,562 RASFF notifications from January 2022 to December 2024, combining descriptive statistics with a role-aware directed network constructed from origin, distribution, and destination fields. The resulting network shows a clear core–periphery structure; it is weakly connected (single undirected component), sparse yet locally cohesive (121 nodes, 869 edges, density = 0.05, average clustering = 0.48). Centrality profiles indicate high-accessibility nodes (closeness), decentralised mediation with uniformly low betweenness (multiple routing paths rather than single brokers), and context-dependent influence (eigenvector). These patterns support network analysis as a preliminary screening tool to prioritise surveillance, cross-border coordination, and follow-up assessment before deploying finer-grained risk models. Addressing gaps in prior work that relied on partial exports, we use complete extraction for the study window and release a freely accessible, analysis-ready dataset for 2022–2024 with full role fields, alongside an integrated resource harmonising recent and historical RASFF notifications. The findings provide actionable guidance for improving surveillance coverage, shortening inter-module pathways, strengthening local cohesion, and enhancing transparency and cooperation in EU agri-food safety governance.

Authors

Institutions

Publication Details

Journal
Discover Food
Published
2025-12-20
DOI
https://doi.org/10.1007/s44187-025-00769-w
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Network analysis as a preliminary screening tool for food safety risk assessment using European Union RASFF notifications from 2022 to 2024

Parthiban Angamuthu, C Shinyclimensa
Discover Food
Data-Driven Disease Surveillance
article

Network analysis as a preliminary screening tool for food safety risk assessment using European Union RASFF notifications from 2022 to 2024

Parthiban Angamuthu, C Shinyclimensa
article en

Abstract

Food safety remains a core priority in the European Union, where dense trade networks demand rapid detection and coordinated response. The Rapid Alert System for Food and Feed (RASFF) enables near-real-time exchange of hazard information among EU member states and associated countries. This study analyses 14,562 RASFF notifications from January 2022 to December 2024, combining descriptive statistics with a role-aware directed network constructed from origin, distribution, and destination fields. The resulting network shows a clear core–periphery structure; it is weakly connected (single undirected component), sparse yet locally cohesive (121 nodes, 869 edges, density = 0.05, average clustering = 0.48). Centrality profiles indicate high-accessibility nodes (closeness), decentralised mediation with uniformly low betweenness (multiple routing paths rather than single brokers), and context-dependent influence (eigenvector). These patterns support network analysis as a preliminary screening tool to prioritise surveillance, cross-border coordination, and follow-up assessment before deploying finer-grained risk models. Addressing gaps in prior work that relied on partial exports, we use complete extraction for the study window and release a freely accessible, analysis-ready dataset for 2022–2024 with full role fields, alongside an integrated resource harmonising recent and historical RASFF notifications. The findings provide actionable guidance for improving surveillance coverage, shortening inter-module pathways, strengthening local cohesion, and enhancing transparency and cooperation in EU agri-food safety governance.

Discover FoodVol. 6(1)
Vellore Institute of Technology University (IN)
Openalex Percentile: Top 47%
Data-Driven Disease Surveillance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Network analysis as a preliminary screening tool for food safety risk assessment using European Union RASFF notifications from 2022 to 2024 — Parthiban Angamuthu, C Shinyclimensa · Discover Food (2025) | TGRS Research Map | TGRS