ABM - Agent Based Model Simulation Framework

A high-performance, flexible and extensible framework to develop continuous-time agent based models. Its high performance allows it to simulate millions of agents efficiently. Agents are defined by their states (arbitrary R lists). The events are handled in chronological order. This avoids the multi-event interaction problem in a time step of discrete-time simulations, and gives precise outcomes. The states are modified by provided or user-defined events. The framework provides a flexible and customizable implementation of state transitions (either spontaneous or caused by agent interactions), making the framework suitable to apply to epidemiology and ecology, e.g., to model life history stages, competition and cooperation, and disease and information spread. The agent interactions are flexible and extensible. The framework provides random mixing and network interactions, and supports multi-level mixing patterns. It can be easily extended to other interactions such as inter- and intra-households (or workplaces and schools) by subclassing an R6 class. It can be used to study the effect of age-specific, group-specific, and contact- specific intervention strategies, and complex interactions between individual behavior and population dynamics. This modeling concept can also be used in business, economical and political models. As a generic event based framework, it can be applied to many other fields. More information about the implementation and examples can be found at <https://github.com/junlingm/ABM>.