Center for Global Health, Health Sciences Center, University of New Mexico, Albuquerque, New Mexico, United States of America.
PLoS One. 2012;7(6):e39778. doi: 10.1371/journal.pone.0039778. Epub 2012 Jun 25.
To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure.
Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) 'connectivity', a model that integrated bio-physical concepts (the agent's transmission cycle, road topology) into indicators designed to measure networks ('nodes' or infected sites with short- and long-range links), and 2) 'contacts', which focused on infected individuals but did not assess connectivity.
THE CONNECTIVITY MODEL SHOWED FIVE NETWORK PROPERTIES: 1) spatial aggregation of cases (disease clusters), 2) links among similar 'nodes' (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a "20:80" pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads.
Geo-temporal constructs of Network Theory's nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended.
为了有效控制传染病的地理传播，需要确定其特性。为了检验快速微生物传播不仅需要易感宿主，还需要一个预先存在的、连接的网络，我们探索了旨在揭示与疾病传播相关的网络特性的构建体，其中包括道路结构。
利用从 2001 年乌拉圭口蹄疫期间所有宿主易感（哺乳动物感染口蹄疫病毒）和 2006 年尼日利亚鸟类感染 H5N1 禽流感期间收集的地理时空数据，比较了两种模型：1）“连通性”模型，该模型将生物物理概念（传播周期）纳入旨在测量网络的指标中（具有短程和远程连接的“节点”或感染地点）；2）“接触”模型，该模型侧重于感染个体，但不评估连通性。
连通性模型显示了五个网络特性：1）病例的空间聚集（疾病集群）；2）类似“节点”之间的联系（聚类）；3）类似节点的同时激活（同步性）；4）从高连通性节点到低连通性节点的疾病流动（方向性）；5）少数节点占大多数病例（“20:80”模式）。在这两种动物传染病中，1）并非所有原发性病例都有联系，但至少有一个原发性病例有联系；2）高度连通的小区域（节点）占大多数病例；3）区分了几个节点类别；4）接触模型，该模型假设所有原发性病例都相同，仅捕获了连通性模型识别的病例数的一半。当一起评估时，同步性和方向性特性解释了传染病何时何地传播。
网络理论节点和链接的地理时空构建体在快速传播的传染病中得到了回顾性验证。它们区分了病例、节点和网络的类别，生成了可用于修改理论和优化控制措施的信息。建议进行前瞻性研究，考虑连接网络等爆发前的预测因素。
