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.
Bibliographical noteFunding Information:
We would like to express our thanks to the swine producers and practitioners for sharing their data. Funding. This study was partially funded by the Swine Health Information Center (SHIC). Funding was also provided by the joint NIFA-NSF-NIH Ecology and Evolution of Infectious Disease award 2019-67015-29918, the Agriculture and Food Research Initiative Competitive grant no. 2018-68008-27890 from the USDA National Institute of Food and Agriculture, the University of Minnesota, and the University of Exeter. GR was supported by BBSRC GRANT BB/P010598/1.
- animal movement
- infectious disease
- multilayer networks
- network analysis