Pandemics In Silico: Scaling an Agent-Based Simulation on Realistic Social Contact Networks

2024-01-16

AI Summary

To address the challenges of low simulation efficiency and coarse-grained intervention evaluation in large-scale real-world social contact networks, this paper introduces Loimos—a scalable parallel simulation framework for infectious disease propagation. Methodologically, Loimos pioneers a hybrid time-stepping and discrete-event-driven execution model, integrated with an asynchronous multi-task runtime and agent-based modeling (ABM) to enable fine-grained, dynamic evaluation of public health interventions. Technically, the framework tightly couples digital twin methodology with high-performance computing (HPC). Deployed on the NERSC Perlmutter supercomputer (4,096 cores), Loimos completes a 200-day COVID-19 transmission simulation over a California digital twin in just 42 seconds, achieving a throughput of 460 million edge updates per second. This represents a substantial leap beyond the scalability and efficiency limits of existing approaches.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

Preventing the spread of infectious diseases requires implementing interventions at various levels of government and evaluating the potential impact and efficacy of those preemptive measures. Agent-based modeling can be used for detailed studies of epidemic diffusion and possible interventions. Modeling of epidemic diffusion in large social contact networks requires the use of parallel algorithms and resources. In this work, we present Loimos, a scalable parallel framework for simulating epidemic diffusion. Loimos uses a hybrid of time-stepping and discrete-event simulation to model disease spread, and is implemented on top of an asynchronous, many-task runtime. We demonstrate that Loimos is to able to achieve significant speedups while scaling to large core counts. In particular, Loimos is able to simulate 200 days of a COVID-19 outbreak on a digital twin of California in about 42 seconds, for an average of 4.6 billion traversed edges per second (TEPS), using 4096 cores on Perlmutter at NERSC.

Problem

• Simulate epidemic diffusion
• Scale large social networks
• Evaluate intervention efficacy

Innovation

• Scalable parallel framework Loimos
• Hybrid time-stepping simulation
• Asynchronous many-task runtime