Sequeira Sara C, Sebunia Natalie, Page Jessica R, Lasisi Taiwo, Habing Greg, Arruda Andréia G
Department of Veterinary Preventive Medicine, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, United States of America.
Hodesson Veterinary Medicine Library, The Ohio State University Libraries, Columbus, Ohio, United States of America.
PLoS One. 2025 Jul 21;20(7):e0328518. doi: 10.1371/journal.pone.0328518. eCollection 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.
新兴传染病对动物和人类种群构成的威胁日益增加，这促使人们更深入地研究与移动相关的相互作用如何促进病原体的地理传播。动物移动是口蹄疫和禽流感等疾病传播的关键因素。对动物移动数据进行网络分析已成为识别传播动态和为疾病控制提供信息的有力工具。然而，目前缺乏对其在不同物种间应用的系统评估。本研究通过对现有证据的系统评估来填补这一知识空白。按照PRISMA-ScR指南以及人群、指标测试和目标疾病研究结构进行了一项改进的范围综述。从六个数据库中检索了1975年至2024年发表的文章。纳入标准侧重于明确提及畜禽移动的网络分析研究。利用R语言进行定量分析以及在NVIVO中进行主题分析，为关键网络应用提供了见解。我们对52个国家的203项研究的综述强调，自2006年以来，基于网络的方法稳步增加，特别是在2001年口蹄疫疫情之后。牛（40.3%）是研究最多的物种，其次是猪（33.2%）和家禽（13.0%）。出现了五个主题：网络结构、疫情建模、靶向控制、疫情分析和网络推断。这些应用展示了网络分析在兽医流行病学中的灵活性。然而，由于数据可获取性问题，挑战依然存在，特别是在低收入和中等收入国家。有限的标准化移动数据阻碍了跨国比较和流行病学见解。扩大数据收集、纳入加权连接以及整合经济和地理因素可以增强网络技术。总之，网络分析是识别高风险节点和设计靶向干预措施的有力框架。未来的工作必须改进数据标准化、时间移动动态，并纳入多种传播途径，以充分捕捉移动网络的复杂性及其在病原体传播中的作用。此外，加强行业与学术界的合作对于优化基于网络的策略至关重要。
