A systematic scoping review and thematic analysis: How can livestock and poultry movement networks inform disease surveillance and control at the global scale? - Sara C. Sequeira; Natalie Sebunia; Jessica R. Page; Taiwo Lasisi; Greg Habing; Andréia G. Arruda (PLoS ONE, 2025)
The increasing threat of emerging infectious diseases affecting animal and human populations has prompted closer investigation into how movement-linked interactions contribute to geographic spread of pathogens. Animal movements are a key factor in the spread of diseases like Foot-and-Mouth-Disease and Avian Influenza. Network analysis of animal movement data has become a powerful tool for identifying transmission dynamics and informing disease control. However, a systematic evaluation of its applications across species is lacking. This study addresses this knowledge gap through a systematic evaluation of existing evidence. A modified scoping review was conducted following PRISMA-ScR guidelines and a Population, Index test, and Target condition research structure. Articles published between 1975 and 2024 were retrieved from six databases. Inclusion criteria focused on network analysis research explicitly mentioning livestock and poultry movements. Quantitative analyses in R and thematic analysis in NVIVO provided insights into key network applications. Our review of 203 studies across 52 countries highlighted a steady rise in network-based approaches since 2006, particularly after the 2001 Foot-and-Mouth-Disease outbreak. Cattle (40.3%) were the most studied species, followed by swine (33.2%) and poultry (13.0%). Five themes emerged: network structure, epidemic modeling, targeted control, outbreak analysis, and network inference. These applications demonstrated the flexibility of network analysis in veterinary epidemiology. However, challenges persist due to data accessibility, particularly in low- and middle-income countries. Limited standardized movement data hinder cross-country comparisons and epidemiological insights. Expanding data collection, incorporating weighted connections, and integrating economic and geographic factors could enhance network-techniques. In conclusion, network analysis is a powerful framework for identifying high-risk nodes and designing targeted interventions. Future efforts must improve data standardization, temporal movement dynamics, and incorporate multiple transmission pathways to fully capture the complexity of movement networks and their role in pathogen spread. Moreover, strengthening industry-academic collaborations is crucial for optimizing network-based strategies.