Discrete-Time Spread Processes: Analysis, Identification, and Validation

Models of spread processes over non-trivial networks are commonly motivated by modeling and analysis of biological networks, computer networks, and human contact networks. However, identification of such models has not been explored in detail, and the models have not been validated by real data. In this paper, we present several different spread models from the literature and explore their relationships to each other; for one of these processes, we present a sufficient condition for asymptotic stability of the healthy equilibrium, show that the condition is necessary and sufficient for uniqueness of the healthy equilibrium, and present necessary and sufficient conditions for learning the spread parameters. Finally, we employ data from the United States Department of Agriculture (USDA) to validate an approximation of a well-studied network-dependent susceptible-infected-susceptible (SIS) model by viewing the enrollment in subsidy programs by farmers as a spread process.