In this chapter, we focus on epidemics spreading over networks. Over the last several decades, researchers across multiple communities have studied epidemics, among which are the classical epidemic models that assume that the population is well mixed. These classical models have been shown to be useful for studying epidemic outbreaks in densely connected populations. However, motivated by the need to understand epidemics at a more fine-grained level (encompassing heterogeneity in individual characteristics or contacts), networked models of epidemic spread have started to gain significant attention in recent years. In this chapter, we consider two types of epidemic spreading models that capture the notation of human-in-the-plant. We first provide a background on modeling, analysis, and applications of networked epidemic models. We show that networked epidemic models are capable of tracing the origin of an outbreak, which aids in developing control strategies for eradicating an epidemic. In the second part of this chapter, we discuss how some cyber–physical–human systems (CPHS) can propagate, or hinder, the spread of epidemics over networks. CPHS are composed of a series of interconnected systems that interact with one another. As such, these are extremely appealing for modeling, analyzing, and eradicating epidemics by capturing the impact of infrastructure, economy, and human factors. Next, we highlight two of our recent works that consider the combination of CPHS with epidemics spreading over networks. In the first work, we model an epidemic spreading process over connected communities by coupling the opinions of these communities over a social network. We analyze the influence of the opinions regarding the outbreak on the epidemic spreading process. In the second work, we consider an epidemic spreading process over connected communities with a shared resource (e.g. a water resource, a supermarket, and a metro station). We model the epidemic spreading process by considering the influence of the shared resource and show that the shared resource is critical in determining the shape of the epidemic (i.e. amount of population infected, hospitalized, recovered, etc.). Finally, we conclude by providing insights on potential future research directions.