DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks

TL;DR

DeepABM introduces a scalable, GPU-efficient framework using graph neural networks for large-scale agent-based simulations, demonstrated on COVID-19 modeling with real-time performance.

Contribution

The paper presents DeepABM, a novel framework that combines graph neural networks with agent-based modeling to enable scalable, real-time simulations on large populations.

Findings

DeepABM-COVID models 200 million interactions in 90 seconds.

Supports various COVID-19 interventions like vaccination and quarantine.

Framework is generic and extendable to other agent-based simulations.

Abstract

We introduce DeepABM, a framework for agent-based modeling that leverages geometric message passing of graph neural networks for simulating action and interactions over large agent populations. Using DeepABM allows scaling simulations to large agent populations in real-time and running them efficiently on GPU architectures. To demonstrate the effectiveness of DeepABM, we build DeepABM-COVID simulator to provide support for various non-pharmaceutical interventions (quarantine, exposure notification, vaccination, testing) for the COVID-19 pandemic, and can scale to populations of representative size in real-time on a GPU. Specifically, DeepABM-COVID can model 200 million interactions (over 100,000 agents across 180 time-steps) in 90 seconds, and is made available online to help researchers with modeling and analysis of various interventions. We explain various components of the framework and discuss results from one research study to evaluate the impact of delaying the second dose of the COVID-19 vaccine in collaboration with clinical and public health experts. While we simulate COVID-19 spread, the ideas introduced in the paper are generic and can be easily extend to other forms of agent-based simulations. Furthermore, while beyond scope of this document, DeepABM enables inverse agent-based simulations which can be used to learn physical parameters in the (micro) simulations using gradient-based optimization with large-scale real-world (macro) data. We are optimistic that the current work can have interesting implications for bringing ABM and AI communities closer.