OMNI-RÉUNIS Research Updates

OMNI-RÉUNIS research focuses on filling gaps in Emerging Infectious Diseases (EID) and modelling. Our research leads, co-project investigators and HQPs are developing innovative methods and models to improve data management, identify risks for emergence and spillover, create early warning systems for infectious diseases, and design effective interventions and control strategies to fill gaps in EID. Find the latest research updates on this page.

Discover the Latest Research on Emerging Infectious Disease Modelling

Dr. Junling Ma

University of Victoria

Agent-based simulation (ABM) is a powerful tool for modelling complex systems, but its implementation can be challenging due to its complexity and steep learning curves. To address this, Dr. Junling Ma from the University of Victoria has developed ABM, an R package that simplifies the process of implementing agent-based simulations. ABM provides a flexible, high-performance framework for developing continuous-time agent-based models that can efficiently simulate millions of agents. The framework allows for intuitive specification and customization of agent state transitions, including spontaneous and interaction-caused transitions. In addition, it supports multi-level mixing patterns, such as random mixing, group-specific mixing, and network interactions, making it suitable for epidemiological, ecological, business, economic, and political models. ABM is included in CRAN, the Comprehensive R Archive Network, and more information can be found on the Wiki pages of the GitHub project. With ABM, implementing agent-based simulations becomes more accessible to a wider audience, enabling researchers to study individual behaviours’ ensemble dynamics and generate realistic data when real data are unavailable.