Contact network analysis has become a vital tool for conceptualizing the spread of pathogens in animal populations and is particularly useful for understanding the implications of heterogeneity in contact patterns for transmission. However, the transmission of most pathogens cannot be simplified to a single mode of transmission and, thus, a single definition of contact. In addition, host-pathogen interactions occur in a community context, with many pathogens infecting multiple host species and most hosts being infected by multiple pathogens. Multilayer networks provide a formal framework for researching host-pathogen systems in which multiple types of transmission-relevant interactions, defined as network layers, can be analyzed jointly. Here, we provide an overview of multilayer network analysis and review applications of this novel method to epidemiological research questions. We then demonstrate the use of this technique to analyze heterogeneity in direct and indirect contact patterns amongst swine farms in the United States. When contact among nodes can be defined in multiple ways, a multilayer approach can advance our ability to use networks in epidemiological research by providing an improved approach for defining epidemiologically relevant groups of interacting nodes and changing the way we identify epidemiologically important individuals such as superspreaders.

接触网络分析已成为概念化病原体在动物种群中传播的重要工具，对于理解接触方式中异质性对于传播的影响特别有用。但是，大多数病原体的传播不能简化为单一的传播方式，因此不能简化为单一的接触定义。另外，宿主-病原体相互作用发生在社区环境中，许多病原体感染多种宿主物种，大多数宿主被多种病原体感染。多层网络为研究宿主-病原体系统提供了一个正式的框架，在该框架中可以联合分析多种类型的与传输相关的交互（定义为网络层）。这里，我们提供了多层网络分析的概述，并回顾了这种新颖方法在流行病学研究问题中的应用。然后，我们证明了使用这种技术来分析美国养猪场之间直接和间接接触方式的异质性。当可以多种方式定义节点之间的联系时，多层方法可以通过提供一种改进的方法来定义相互作用节点的流行病学相关组，并改变我们识别流行病学上重要的个体（例如超级传播者）的方式，从而提高我们在流行病学研究中使用网络的能力。