Analysis and Control of Epidemics A survey of spreading processes on complex networks Cameron Nowzari, Victor M. Preciado, and George J. Pappas August 26, 2015 NOTE: This arXiv version contains a table of contents at the end for convenience. This article reviews and presents various solved and open problems in the development, analysis, and control of epidemic models. Proper modeling and analysis of spreading processes has been a longstanding area of research among many different fields including mathematical biology, physics, computer science, engineering, economics, and the social sciences. One of the earliest epidemic models conceived was by Daniel Bernoulli in 1760, which was motivated by studying the spreading of smallpox [1]. In addition to Bernoulli, there were many different researchers also working on mathematical epidemic models around this time [2]. These initial models were quite simplistic and the further development and study of such models dates back to the 1900s [3]–[6], where still simple models were studied to provide insight as to how various diseases can spread through a population. In recent years, we have seen a resurgence of interest in these problems as the concept of ‘networks’ becomes increasingly prevalent in modeling many different aspects of our world today. A more comprehensive review of the history of mathematical epidemiology can be found in [7], [8]. Despite the study of epidemic models having spanned such a long period of time, it is only recently that control engineers have entered the scene. Consequently, there is already a vast body of work dedicated to the development and analysis of epidemic models, but far less that provide proper insight and machinery on how to effectively control these processes. The focus of this article is to provide an introductory tutorial on the latter. We are interested in presenting a relatively concise report for new engineers looking to enter the field of spreading processes on complex networks. This article presents some of the more well-known and recent results in the literature while also identifying numerous open problems that can benefit from the collective knowledge of optimization and control theorists. 1 arXiv:1505.00768v2 [math.OC] 25 Aug 2015