Greiner Ariel, Herrera-Diestra José L, Tildesley Michael, Shea Katriona, Ferrari Matthew
Center for Infectious Disease Dynamics and Department of Biology, Pennsylvania State University, University Park, Pennsylvania, United States of America.
Department of Biology, University of Oxford, Oxford, United Kingdom.
PLoS Comput Biol. 2025 Jul 11;21(7):e1012395. doi: 10.1371/journal.pcbi.1012395. eCollection 2025 Jul.
Foot and Mouth Disease (FMD) affects cloven-hoofed animals globally and has become a major economic burden for many countries around the world. Countries that have had recent FMD outbreaks are prohibited from exporting most meat products; this has major economic consequences for farmers in those countries, particularly farmers that experience outbreaks or are near outbreaks. Reducing the number of FMD outbreaks in countries where the disease is endemic is an important challenge that could drastically improve the livelihoods of millions of people. As a result, significant effort is expended on surveillance; but there is a concern that uninformative surveillance strategies may waste resources that could be better used on control management. Rapid detection through sentinel surveillance may be a useful tool to reduce the scale and burden of outbreaks. In this study, we use an extensive outbreak and cattle shipment network dataset from the Republic of Türkiye to retrospectively test three possible strategies for sentinel surveillance allocation in countries with endemic FMD and minimal existing FMD surveillance infrastructure that differ in their data requirements: ranging from low to high data needs, we allocate limited surveillance to [1] farms that frequently send and receive shipments of animals (Network Connectivity), [2] farms near other farms with past outbreaks (Spatial Proximity) and [3] farms that receive many shipments from other farms with past outbreaks (Network Proximity). We determine that all of these surveillance methods find a similar number of outbreaks - 2-4.5 times more outbreaks than were detected by surveying farms at random. On average across surveillance efforts, the Network Proximity and Network Connectivity methods each find a similar number of outbreaks and the Spatial Proximity method always finds the fewest outbreaks. Since the Network Proximity method does not outperform the other methods, these results indicate that incorporating both cattle shipment data and outbreak data provides only marginal benefit over the less data-intensive surveillance allocation methods for this objective. We also find that these methods all find more outbreaks when outbreaks are rare. This is encouraging, as early detection is critical for outbreak management. Overall, since the Spatial Proximity and Network Connectivity methods find a similar proportion of outbreaks, and are less data-intensive than the Network Proximity method, countries with endemic FMD whose resources are constrained could prioritize allocating sentinels based on whichever of those two methods requires less additional data collection.
口蹄疫（FMD）在全球范围内影响偶蹄类动物，并已成为世界上许多国家的一项主要经济负担。近期发生过口蹄疫疫情的国家被禁止出口大多数肉类产品；这给这些国家的农民带来了重大经济后果，尤其是那些经历过疫情爆发或靠近疫情爆发地区的农民。减少口蹄疫流行国家的疫情爆发次数是一项重要挑战，这可能会极大地改善数百万人的生计。因此，在监测方面投入了大量精力；但人们担心，缺乏信息的监测策略可能会浪费资源，而这些资源本可更好地用于控制管理。通过哨兵监测进行快速检测可能是减少疫情规模和负担的一个有用工具。在本研究中，我们使用来自土耳其共和国的一个广泛的疫情和牲畜运输网络数据集，对在口蹄疫流行且现有口蹄疫监测基础设施极少、数据需求不同的国家进行哨兵监测分配的三种可能策略进行回顾性测试：从低到高的数据需求，我们将有限的监测分配给[1]经常收发动物运输的农场（网络连通性）、[2]靠近其他有过疫情爆发的农场的农场（空间邻近性）以及[3]从其他有过疫情爆发的农场接收大量运输的农场（网络邻近性）。我们确定，所有这些监测方法发现的疫情爆发次数相似——比随机调查农场发现的疫情爆发次数多2至4.5倍。在所有监测工作中，平均而言，网络邻近性方法和网络连通性方法发现的疫情爆发次数相似，而空间邻近性方法发现的疫情爆发次数总是最少。由于网络邻近性方法并不优于其他方法，这些结果表明，对于这一目标，结合牲畜运输数据和疫情爆发数据相比数据密集度较低的监测分配方法仅能带来边际效益。我们还发现，当疫情爆发罕见时，这些方法都能发现更多疫情爆发。这是令人鼓舞的，因为早期检测对于疫情管理至关重要。总体而言，由于空间邻近性方法和网络连通性方法发现的疫情爆发比例相似，且比网络邻近性方法的数据密集度更低，资源受限的口蹄疫流行国家可以根据这两种方法中哪种需要更少的额外数据收集来优先分配哨兵。
