Complex networks and agent-based models of HIV epidemic

Graphical abstractComplexity and complex systems are all around us: from molecular and cellular systems in biology up to economics and human societies. There is an urgent need for methods that can capture the multi-scale spatio-temporal characteristics of complex systems. Recent emphasis has centered on two methods in particular, those being complex networks and agent-based models. In this paper we look at the combination of these two methods and identify "Complex Agent Networks", as a new emerging computational paradigm for complex system modeling. We argue that complex agent networks are able to capture both individual-level dynamics as well as global-level properties of a complex system, and as such may help to obtain a better understanding of the fundamentals of such systems.We formally describe a form of modeling (which we term complex agent networks) that unifies agent-based modeling and complex networks with respect to the individual agent node model and the networks of agent interactions. Apart from the intrinsic complexity of an agent itself, the interactions among agents can be depicted with spatial models such as grid, map and networks (Fig. 1) and we can map the interactions among agents eventually to networks (Fig. 2).Then we apply CANs to modeling infectious diseases as shown in Fig. 3. Persons are described as agents with heterogeneous disease progression, while the dynamic interactions among agents, that can cause infections, are described by networks.Then we apply CANs to modeling infectious diseases as shown in Fig. 3. Persons are described as agents with heterogeneous disease progression, while the dynamic interactions among agents, that can cause infections, are described by networks.Finally we conclude with a summary of significant research issues related to CANs. Display Omitted HighlightsFormally describes a form of modeling that unifies agent-based modeling and complex networks.Captures both individual-level and global-level dynamics of a system.Describes a system's interaction patterns in networks and its agency in ABM.Reviews existing work from epidemiology, ecology and economics.Presents a detailed example that uses CANs to study infectious disease spreading. Complexity and complex systems are all around us: from molecular and cellular systems in biology up to economics and human societies. There is an urgent need for methods that can capture the multi-scale spatio-temporal characteristics of complex systems. Recent emphasis has centered on two methods in particular, those being complex networks and agent-based models. In this paper we look at the combination of these two methods and identify "Complex Agent Networks", as a new emerging computational paradigm for complex system modeling. We argue that complex agent networks are able to capture both individual-level dynamics as well as global-level properties of a complex system, and as such may help to obtain a better understanding of the fundamentals of such systems.