Simulating the Effect of Network Structure on the Spread of Disease

This simulation study was my final project for STOR-672: Simulation Modeling and Analysis, a course I took with Dr. Mariana Olvera-Cravioto at the University of North Carolina in the Spring of 2020. The model in this paper demonstrates how decreasing the average degree of the nodes in a random graph (i.e. enacting a policy like social distancing) can slow the spread of disease in a meaningful way. The graphs themselves are generated according to an Erased Configuration model. I chose the model parameters in the simulation to reflect the conditions of the spread of COVID-19 through a moderately sized community. The code for generating the simulation experiments is available here; however, the use of a cluster with a Slurm Workload Manager is required to conduct simulation experiments at any kind of meaningful scale.