Simulating Complex Agent-Based Model with epiworldR: A fast and flexible ABM framework
George G Vega Yon, Derek Meyer
Despite significant medical advances, infectious diseases continue to prevail worldwide, accounting for over 17 million deaths yearly (WHO). This workshop introduces epiworldR, an R package that provides a fast (C++ backend) and highly-customizable framework for building network-based transmission/diffusion agent-based models [ABM]. This package provides valuable information that may aid in making informed, evidence-based policy decisions for the general population and performing complex simulation studies. epiworldR delivers a flexible tool that can capture transmission/diffusion dynamics complexity resulting from agents’ heterogeneity, network structure, transmission dynamics, environmental factors (e.g., policies,) and many other elements. Some key features of epiworldR are the ability to construct multi-disease models (e.g., models of competing multi-pathogens/multi-rumor,) design mutating pathogens, architect population-level interventions, and build models with an arbitrary number of compartments/states (beyond SIR/SEIR.) Moreover, epiworldR is really fast; for example, simulating a SIR model with 100,000 agents for 100 days takes less than ⅓ of a second.
The workshop will be 100% hands-on and will feature examples of simulating multi-disease/rumor models, policy intervention models, mutating variants, and creating models with arbitrary compartments. Participants should have a working knowledge of R (e.g., some experience with statnet).