Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, North Carolina, USA.
Departamento de Defesa Agropecuária, Secretaria da Agricultura, Pecuária e Desenvolvimento Rural (SEAPDR), Porto Alegre, Brazil.
Transbound Emerg Dis. 2021 May;68(3):1663-1675. doi: 10.1111/tbed.13841. Epub 2020 Oct 4.
Tracking animal movements over time may fundamentally determine the success of disease control interventions. In commercial pig production growth stages determine animal transportation schedule, thus it generates time-varying contact networks showed to influence the dynamics of disease spread. In this study, we reconstructed pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. The static networks view did not capture time-respecting movement pathways. For this reason, we propose a time-dependent network approach. A susceptible-infected model was used to spread an epidemic over the pig network globally through the temporal between-farm networks, and locally by a stochastic model to account for within-farm dynamics. We propagated disease to calculate the cumulative contacts as a proxy of epidemic sizes and evaluate the impact of network-based disease control strategies in the absence of other intervention alternatives. The results show that targeting 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our modelling results indicated that independently from where initial infections were seeded (i.e. independent, commercial farms), the epidemic sizes and the number of farms needed to be targeted to effectively control disease spread were quite similar; indeed, this finding can be explained by the presence of contact among all pig operation types The proposed strategy limited the transmission the total number of secondarily infected farms to 29, over two simulated years. The identified 1,000 farms would benefit from enhanced biosecurity plans and improved targeted surveillance. Overall, the modelling framework provides a parsimonious solution for targeted disease surveillance when temporal movement data are available.
追踪动物随时间的移动轨迹可能从根本上决定疾病控制干预措施的成败。在商业养猪生产中，生长阶段决定了动物运输计划，从而产生了随时间变化的接触网络，这些网络被证明会影响疾病传播的动态。在这项研究中，我们从 2017 年到 2018 年重建了巴西一个州的猪网络，其中包括 351519 次移动和 4800 万头运输猪。静态网络视图无法捕捉到尊重时间的移动路径。因此，我们提出了一种基于时间的网络方法。我们使用易感染-感染模型通过农场间的时间网络在猪网络上传播全球流行病，通过随机模型在农场内传播以解释农场内的动态。我们传播疾病来计算累积接触作为流行病规模的代理，并在没有其他干预替代方案的情况下评估基于网络的疾病控制策略的影响。结果表明，针对按度排名前 1000 个农场进行目标定位将是足够且可行的，可以大大减少疾病的传播。我们的建模结果表明，无论初始感染在哪里发生（即独立的商业农场），有效控制疾病传播所需的流行病规模和目标农场数量都非常相似；事实上，这种发现可以用所有猪养殖类型之间存在接触来解释。所提出的策略将总二次感染农场的传播数量限制在 29 个以内，在两个模拟年内。这 1000 个农场将受益于加强生物安全计划和改进的有针对性的监测。总的来说，当有时间移动数据时，该建模框架为有针对性的疾病监测提供了一个简洁的解决方案。
