The emergence the COVID-19 pandemic and its continued prevalence in the human population has illustrated significant obstacles to infectious disease surveillance and prevention on a global scale. While a great deal of research has been done on the impact of globalization on human-human transmission of pathogens, significantly less focus has been placed on the role that the international trade of food commodities might play in the spread of pathogens. Additionally, the rising threat of climate change poses significant challenges to both the stability of the global food supply, and to the resilience of staple crops to pathogens capable of impacting humans. A model was designed to improve surveillance of these pathogens by analyzing their associated epidemiological data to determine country and commodity of origin. To that end, in this work we illustrate a method for constructing directed trade-networks (DTNs) of the specific trade routes employed by countries when importing a specific commodity. This methodology utilizes the unparalleled accuracy of the UN Comtrade Database to provide the underlying data for the network construction process. We then consider the properties of these networks and describe how to use our analysis framework to identify potential source commodities and countries of a given pathogen. This is done through comparison of network topology and international infectious disease datasets. Two main approaches are explored, namely correlation measures and structural break methods between the networks and the pathogen related dataset of interest. The analyses described will support future evaluation of trade-related, infectious disease events in their ability to identify the likely source of a particular pathogen. Furthermore, the application of this approach to model commodity-linked pathogens could be a versatile tool for informing policymakers on the potential epidemiological threats posed by trade policies.